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The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a 'sequence' modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management. [ABSTRACT FROM AUTHOR]

Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers. [ABSTRACT FROM AUTHOR]

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model's overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains. [ABSTRACT FROM AUTHOR]

Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters. [ABSTRACT FROM AUTHOR]

Heavy rainfall is a major factor for landslide triggering. Here, we present an inventory of 47,523 landslides triggered by two precipitation episodes that occurred in May 2023 in the Emilia-Romagna and conterminous regions (Italy). The landslides are manually mapped from a visual interpretation of satellite images and are mainly triggered by the second rainfall episode (16–17 May 2023); the inventory is entirely original, and the mapping is supplemented with field surveys at a few selected locations. The main goal of this paper is to present the dataset and to investigate the landslide distribution with respect to triggering (precipitation) and predisposing (land use, lithology, slope and distance from roads) factors using a statistical approach. The landslides occurred more frequently on steeper slopes and for the land use categories of "bare rocks and badlands" and woodlands. A weaker positive correlation is found for the lithological classes: silty and flysch-like units are more prone to host slope movements. The inventory presented here provides a comprehensive picture of the slope movements triggered in the study area and represents one of the most numerous rainfall-induced landslide inventories on a global scale. We claim that the inventory can support the validation of automatic products and that our results on triggering and predisposing factors can be used for modeling landslide susceptibility and more broadly for hazard purposes. [ABSTRACT FROM AUTHOR]

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management. [ABSTRACT FROM AUTHOR]

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features. [ABSTRACT FROM AUTHOR]

Remote sensing plays an increasingly important role in the investigation of natural hazards, not only by obtaining specific data related to hazards, but also by realizing targeted research by combining with other data and/or technologies. Small-scale landslide hazard chain events occur frequently in mountainous areas with fragile geological environments and have strong destructive effects, yet have been somewhat understudied. This paper analyzes the Zhoujiaba (ZJB) landslide hazard chain that occurred in Longnan City on 18 August 2020. On the basis of the comprehensive application of multi-source remote sensing data, combined with time-series InSAR technology, electrical resistivity tomography (ERT), and numerical simulations, we studied the formation mechanism, damming characteristics, and potential outburst scenarios of this event. Our research suggests that geological structure and strong natural weathering are the preconditions for landslide development, which is eventually induced by extreme rainfall. Specific topographic conditions determine the rapid sliding and accumulation of landslide materials, and ultimately result in river damming. Our simulation results showed that a flood, rather than a debris flow, will be the result of dam outburst. When the simulated upstream inflow is 1.5 times that when the landslide occurred, 68% of the downstream village area will be flooded. The artificial spillway can effectively reduce the scale of the potential outburst flood, but there remains a risk of dam failure owing to the shallow depth. Our study of the hazard chain of a small-scale landslide using a combination of methods will provide a valuable reference for the analysis and treatment of similar hazard chains. [ABSTRACT FROM AUTHOR]

Landslides are an example of severe natural disasters that occur worldwide and generate many harmful effects that can affect the stability and development of society. A better-quality susceptibility mapping technique for the landslide risk is crucial for mitigating landslides. However, the use of assemblages of multivariate statistical methods is still uncommon in Indonesia, particularly in the Kepahiang Regency of Bengkulu Province. Therefore, the objective of this study was to provide an improved framework for creating landslide susceptibility map (LSM) using multivariate statistical methods, i.e., the analytical hierarchy process (AHP) method, the simple additive weighting (SAW) method and the frequency ratio (FR) method. In this study, we established a landslide inventory considering 15 causative factors using the area under the curve (AUC) validation method and another evaluation technique. The performance of each causative factor was evaluated using multicollinearity and Pearson correlation analysis with regression-based ranking. The LSM results showed that the most susceptible areas were located in the districts of Kabawetan, Kepahiang, and Tebat Karai. The high landslide risk in these areas could be attributed to the slope conditions in mountainous regions, which are characterized by high annual rainfall and seismic activity. The AUC training values of the AHP, SAW, and FR methods were 0.866, 0.838, and 0.812, respectively. Then, on the validation dataset, the AHP method yielded the highest AUC value (0.863), followed by the SAW (0.833) and FR (0.807) methods. Moreover, the AHP method provided a higher accuracy value, which suggests that the AHP method is more suitable than the other methods. Therefore, our research indicated that all algorithm methods generate a positive impact and greatly improve landslide susceptibility evaluation, especially for the preparation of landslide damage assessments in this study area. Finally, the method proposed in this study could improve the feasibility of LSM and provide support for Indonesian government decision-makers in arranging hazard mitigation measures in the Kepahiang Regency, Indonesia. Key points: In this paper, we present a framework for assembling multivariate statistical methods and developing landslide susceptibility mapping of Kepahiang, Bengkulu Province, Indonesia. The analytical hierarchy process provides the highest landslide susceptibility mapping accuracy and the lowest uncertainties in the study region. Slope, rainfall, and peak ground acceleration are the most essential spatial determinants of the changes in Kepahiang, Bengkulu Province, Indonesia. [ABSTRACT FROM AUTHOR]

The most frequent and noticeable natural calamity in the Karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram Highway, particularly during monsoons, causing a major loss of life and property. Therefore, it is necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper's major goal is to provide new integrative models for assessing landslide susceptibility in a prone area in the north of Pakistan. To achieve this, the training of an artificial neural network (ANN) was supervised using metaheuristic and Bayesian techniques: Particle Swarm Optimization (PSO) algorithm, Genetic algorithm (GA), Bayesian Optimization Gaussian Process (BO_GP), and Bayesian Optimization Tree-structured Parzen Estimator (BO_TPE). In total, 304 previous landslides and the eight most prevalent conditioning elements were combined to form a geospatial database. The models were hyperparameter optimized, and the best ones were employed to generate susceptibility maps. The obtained area under the curve (AUC) accuracy index demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying ANNs for landslide mapping, susceptibility analysis, and forecasting were studied in this research, and it was observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE were relatively small, ranging from 0.32% to 1.84%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it is important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally, in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the Karakoram Highway (KKH). The algorithms considered include Information Gain, Variance Inflation Factor, OneR Classifier, Subset Evaluators, principal components, Relief Attribute Evaluator, correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Cultural heritage is one of the most exceptional resources characterizing the Italian territory. Archaeological heritage, i.e., the archaeological sites with different types of archaeological artifacts, strongly contributes to enriching the national and international cultural heritage. Nevertheless, it is constantly exposed to external factors, such as natural deterioration, anthropic impact, and climate-related hazards, which may compromise its conservation. In Italy, many archaeological areas are affected by significant soil settlements that involve a large part of monuments. This paper focuses on the landslide hazard assessment of the archaeological site of Pietrabbondante (Molise region, Italy). The impact of the expected rainfall regimes, according to the EURO-CORDEX projections, on slope stability conditions were evaluated through the application of a physically based model that couples a hydraulic and a mechanical model to evaluate slope stability evolution due to pore pressure changes. Given the unavoidable lack of knowledge of the geotechnical soil properties in an archaeological heritage area, the proposed method considered the random uncertainty of soil parameters by means of a probabilistic approach in order to assess the stability conditions in terms of probability of occurrence of a landslide. The results of this study provide a reference for the safety assessment and preventive conservation of archaeological areas characterized by high cultural value. [ABSTRACT FROM AUTHOR]

On 11 October and 3 November 2018, two large landslides occurred in Baige Village, Tibet, China, forcing the Jinsha River to be cut off and form a dammed lake, resulting in massive economic damages and deaths. This paper uses ground-based radar (GBR) and spaceborne interferometric synthetic aperture radar (InSAR) technologies to perform dynamic monitoring of the Baige landslide. Firstly, the GBR results suggest that the cumulative deformation from 4 to 10 December 2018 was 1.4 m, and the landslide still exhibits a risk of instability. Secondly, with the Sentinel-1A ascending and descending orbit images from December 2018 to February 2022, the InSAR-stacking technology assisted by the generic atmospheric correction online service (GACOS) and the multidimensional small baseline subset (MSBAS) method are utilized to obtain the annual deformation velocity and cumulative deformation in the satellite radar line of sight (LOS) direction of the landslide. Finally, according to the spatial–temporal deformation characteristics of feature points, combined with optical images, field investigation, and geological conditions, the development trend and inducing factors of the Baige landslide are comprehensively analyzed. It is shown that the Baige landslide is in constant motion at present, and the deformation is spreading from the slope to its right side. This research establishes a framework of combining emergency monitoring (i.e., GBR) with long-term monitoring (i.e., spaceborne InSAR). The framework is more conducive to obtaining the deformation and evolution of landslides, providing a greater possibility for studying the development trend and risk assessment of landslides, and assisting in reducing or even avoiding the losses caused by landslides. [ABSTRACT FROM AUTHOR]

This paper presents a significant advancement in the understanding of tsunamigenic landslide hazard across the length of the Makassar Strait in Indonesia. We use statistical emulation across the length of the continental slope to conduct a probabilistic assessment of tsunami hazard on a regional scale, across 14 virtual coastal gauges. Focusing on the potential maximum wave amplitudes (distance between the wave crest and the still‐water level) from possible tsunamigenic landslide events, we generate predictions from Gaussian Process emulators fitted to input‐outputs from 50 training scenarios. We show that the most probable maximum wave amplitudes in the majority of gauges are between 1 and 5 m, with the maximum predicted amplitudes reaching values of up to 10 m on the eastern coast, and up to 50 m on the western coast. We also explore the potential use of Gaussian multivariate copulas to sample emulator prediction input values to create a more realistic distribution of volumes along the continental slope. The novel use of statistical emulation across a whole slope enables the probabilistic assessment of tsunami hazard due to landslides on a regional scale. This area is of key interest to Indonesia since the new capital will be established in the East Kalimantan region on the western side of the Makassar Strait. Plain Language Summary: This work is the first to use statistical models to conduct a landslide‐generated tsunami hazard assessment for the whole of the Makassar Strait in Indonesia. From 50 simulated landslide‐tsunami scenarios, we fit statistical models (known as emulators) to then predict the likelihood and maximum wave height of potential tsunami waves in 14 different locations along the Makassar continental slope. These predictions show that the most likely wave heights were between 1 and 5 m, with the highest predicted waves reaching up to 10 m on the eastern coast and up to 50 m on the western coast. The results of this study are particularly important because Indonesia has announced the relocation of its new capital city to the western side of the Makassar Strait, within the study area. Key Points: This is the first use of emulation for tsunamigenic landslide hazard assessment worldwide integrating both uncertain location and sizeThis is the first tsunamigenic landslide hazard assessment for the whole Makassar Strait regionProbable tsunami wave amplitudes reach up to 50 m on the West coast and around 14 m where the Indonesian capital will be relocated [ABSTRACT FROM AUTHOR]

A steel-wire wound reinforced thermoplastic pipe (SWW-RTP) has been widely utilized in many industrial areas, and a soil landslide is an inevitable hazardous extreme condition for the SWW-RTP as it is usually buried underground. It is imperative to study the mechanical failure behavior and the failure criterion of the SWW-RTP under the combination of internal pressure and soil landslide conditions, and this paper is the first study to investigate the topic. In this paper, groups of stress–strain curves of high-density polyethylene (HDPE) and steel wires were obtained by uniaxial tensile tests at different strain rates, with the help of a Digital Image Correlation device (DIC). A rate-dependent constitutive model was employed to represent the mechanical behavior of the HDPE and to help deduce the stress–strain curve of the HDPE under the required strain rate, estimated from the static simplification of the dynamic soil landslide. Afterwards, a finite element model of the SWW-RTP, embedded in a cubic of soil, was established with the software ABAQUS. The SWW-RTP model was composed of HDPE solid elements, embedded with steel-wire truss elements, and the soil was characterized with the extended Drucker–Prager model. A quartic polynomial displacement distribution was applied to the soil model to represent the soil landslide. Then, the mechanical response of the SWW-RTP was analyzed. It was found that the failure criterion of the HDPE yield was more suitable for the pipe subjected to internal pressure and soil landslide conditions, instead of the steel-wire strength failure criterion always used in traditional research on the SWW-RTP. Further, the influence of landslide width, internal pressure and steel-wire number were discussed. The larger the width of the landslide area, the gentler the deformation of the pipeline; this resulted in an increase in the maximum landslide and a decrease in the maximum curvature with the width of the landslide area. The relatively high internal pressure was beneficial to the safety of the SWW-RTP under landslide, because the internal pressure could increase the stiffness of the pipeline. The number of steel wires had a limited influence on the maximum landslide required for the SWW-RTP's failure. This work can be useful for the design and safe assessment of the SWW-RTP under internal pressure and soil landslide conditions. [ABSTRACT FROM AUTHOR]

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments. [ABSTRACT FROM AUTHOR]

The rapidly warming climate on the Qinghai–Tibet Plateau (QTP) leads to permafrost degradation, and the thawing of ice-rich permafrost induces land subsidence to facilitate the development of thermokarst lakes. Thermokarst lakes exacerbate the instability of permafrost, which significantly alters regional geomorphology and hydrology, affecting biogeochemical cycles. However, the spatial distribution and future changes in thermokarst lakes have rarely been assessed at large scales. In this study, we combined various conditioning factors and an inventory of thermokarst lakes to assess the spatial distribution of susceptibility maps using machine-learning algorithms. The results showed that the extremely randomized trees (EXT) performed the best in the susceptibility modeling process, followed by random forest (RF) and logistic regression (LR). According to the assessment based on EXT, the high- and very high-susceptibility area of the present (2000–2016) susceptibility map was 196,222 km2, covering 19.67% of the permafrost region of the QTP. In the future (the 2070s), the area of the susceptibility map was predicted to shrink significantly under various representative concentration pathway scenarios (RCPs). The susceptibility map area would be reduced to 37.06% of the present area in RCP 8.5. This paper also performed correlation and importance analysis on the conditioning factors and thermokarst lakes, which indicated that thermokarst lakes tended to form in areas with flat topography and high soil moisture. The uncertainty of the susceptibility map was further assessed by the coefficient of variation (CV). Our results demonstrate a way to study the spatial distribution of thermokarst lakes at the QTP scale and provide a scientific basis for understanding thermokarst processes in response to climate change. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samples tend to be much smaller than non-landslide samples, is often overlooked. This problem is often one of the important factors affecting the performance of landslide susceptibility models. In this paper, we take the Wanzhou district of Chongqing city as an example, where the total number of data sets is more than 580,000 and the ratio of positive to negative samples is 1:19. We oversample or undersample the unbalanced landslide samples to make them balanced, and then compare the performance of machine learning models with different sampling strategies. Three classic machine learning algorithms, logistic regression, random forest and LightGBM, are used for LSA modeling. The results show that the model trained directly using the unbalanced sample dataset performs the worst, showing an extremely low recall rate, indicating that its predictive ability for landslide samples is extremely low and cannot be applied in practice. Compared with the original dataset, the sample set optimized through certain methods has demonstrated improved predictive performance across various classifiers, manifested in the improvement of AUC value and recall rate. The best model was the random forest model using over-sampling (O_RF) (AUC = 0.932). [ABSTRACT FROM AUTHOR]

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China's Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K-means clustering was used to purify the factor data factors before data-driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K-means method is of great significance for the drawing of landslide sensitivity map. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets for constituent classifiers of the ensemble model, and a split-point and attribute reduced classifier (SPAARC) decision tree algorithm to build each classifier. To optimize hyperparameters of the ensemble model, a hybridization of biogeography-based optimization (BBO) and differential evolution (DE) algorithms is adopted. The land-slide database for the study area includes 114 landslide locations, 114 non-landslide locations, and ten influencing factors: elevation, slope, curvature, aspect, relief amplitude, soil type, geology, distance to faults, distance to roads, and distance to rivers. The database was used to build and verify the BBO-DE-StreeEns model, and standard statistical metrics, namely, positive predictive value (PPV), negative predictive value (NPV), sensitivity (Sen), specificity (Spe), accuracy (Acc), Fscore, Cohen's Kappa, and the area under the ROC curve (AUC), were calculated to evaluate prediction power. Logistic regression, multi-layer perceptron neural network, support vector machine, and SPAARC were used as benchmark models. The results show that the proposed model outperforms the benchmarks with a high prediction power (PPV = 90.3%, NPV = 83.8%, Sen = 82.4%, Spe = 91.2%, Acc = 86.8%, Fscore = 0.862, Kappa = 0.735, and AUC = 0.940). Therefore, the BBO-DE-StreeEns method is a promising tool for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation. [ABSTRACT FROM AUTHOR]

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV–LR, IV–SVM, IV–RF, FR–LR, FR–SVM, and FR–RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR–RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR–RF coupling model had the highest accuracy. Under the optimal model FR–RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall. [ABSTRACT FROM AUTHOR]

Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed; specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction. [ABSTRACT FROM AUTHOR]

It is still a challenging task to implement efficient methods for reliability analysis, especially for complex engineering systems that have implicit limit state surfaces and multiple failure modes. In this paper, an adaptive Kriging-assisted system reliability method is proposed to solve system reliability assessment problems. The proposed method uses an adaptive Kriging-based trust region to obtain the design point of each component failure mode based on the first-order reliability method in the first stage. By adding barrier functions to the potential limit state functions (LSFs) around the candidate design point, a new limit state function is obtained and used for the next failure mode identification in the second stage. After identifying all the potential failure modes, the system reliability assessment can be initially approximated by direct integration or binomial bounds theory. The accuracy of approximating failure probability can be enhanced by importance sampling or active learning in the third stage. Two case studies with quantitative landslide risk assessment are used to show the efficiency and accuracy of the proposed method. With the help of the smoothed-particle hydrodynamics method (SPH), the large deformation behaviours which may induce a strong nonlinear limit state surface can be used as the indicators of the failure consequences. The results show that the method proposed in this paper is capable of solving high non-linearity implicit LSFs. It also exhibits potential in handling problems with small failure probabilities. [ABSTRACT FROM AUTHOR]

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas. [ABSTRACT FROM AUTHOR]

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM. SciMAT tool initially employs cluster analysis and thematic evolution analysis to pinpoint the current popular themes and evolutionary trends of landslide susceptibility. The main trajectory of knowledge lies in the progression of landslide modeling methods. The latest trend in landslide susceptibility research is centered on the use of deep learning techniques, particularly convolutional neural networks, to achieve precise risk zonation. The study aims to highlight critical research directions, predict the future of LSM technology, and provide guidance for scholars. [ABSTRACT FROM AUTHOR]

Transferability of knowledge from well-investigated areas to a new study region is gaining importance in landslide hazard research. Considering the time-consuming compilation of landslide inventories as a prerequisite for landslide susceptibility mapping, model transferability can be key to making hazard-related information available to stakeholders in a timely manner. In this paper, we compare and combine two important transfer-learning strategies for landslide susceptibility modeling: case-based reasoning (CBR) and domain adaptation (DA). Care-based reasoning gathers knowledge from previous similar situations (source areas) and applies it to solve a new problem (target area). Domain adaptation, which is widely used in computer vision, selects data from a source area that has a similar distribution to the target area. We assess the performances of single- and multiple-source CBR, DA, and CBR–DA strategies to train and combine landslide susceptibility models using generalized additive models (GAMs) for 10 study areas with various resolutions (1, 10, and 25 m) located in Austria, Ecuador, and Italy. The performance evaluation shows that CBR and combined CBR–DA based on our proposed similarity criterion were able to achieve performances comparable to benchmark models trained in the target area itself. Particularly the CBR strategies yielded favorable results in both single- and multi-source strategies. Although DA tended to have overall lower performances than CBR, it had promising results in scenarios where the source–target similarity was low. We recommend that future transfer-learning research for landslide susceptibility modeling can build on the similarity criterion we used, as it successfully helped to transfer landslide susceptibility models by identifying suitable source regions for model training. [ABSTRACT FROM AUTHOR]

In the past 50 years, a large variety of statistically based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, which are applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations, and several approaches to evaluate the models' performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modeling and zonation is still missing. The literature search revealed that several software program and tools are available to evaluate regional slope stability using physically based analysis, but only a few use statistically based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically based landslide susceptibility modeling which integrates LAND-SE. LAND-SUITE completes and extends LAND-SE, adding functionalities to (i) facilitate input data preparation, (ii) perform preliminary and exploratory analysis of the available data, and (iii) test different combinations of variables and select the optimal thematic/explanatory set. LAND-SUITE provides a tool to assist the user during the data preparatory phase and to perform diversified statistically based landslide susceptibility applications. [ABSTRACT FROM AUTHOR]

Predicting the susceptibility of a specific part of a landslide (SSPL) involves predicting the likelihood that the part of the landslide (e.g., the entire landslide, the source area, or the scarp) will form in a given area. When predicting SSPL, the landslide samples are far less than the non-landslide samples. This class imbalance makes it difficult to predict the SSPL. This paper proposes an advanced artificial intelligence (AI) model based on the dice-cross entropy (DCE) loss function and XGBoost (XGBDCE) or Light Gradient Boosting Machine (LGBDCE) to ameliorate the class imbalance in the SSPL prediction. We select the earthquake-induced landslides from the 2018 Hokkaido earthquake as a case study to evaluate our proposed method. First, six different datasets with 24 landslide influencing factors and 10,422 samples of a specific part of the landslides are established using remote sensing and geographic information system technologies. Then, based on each of the six datasets, four landslide susceptibility algorithms (XGB, LGB, random-forest (RF) and linear discriminant analysis (LDA)) and four class balancing methods (non-balance (NB), equal-quantity sampling (EQS), inverse landslide-frequency weighting (ILW), and DCE loss) are applied to predict the SSPL. The results show that the non-balanced method underestimates landslide susceptibility, and the ILW or EQS methods overestimate the landslide susceptibility, while the DCE loss method produces more balanced results. The prediction performance of the XGBDCE (average area under the receiver operating characteristic curve (0.970) surpasses that of RF (0.956), LGB (0.962), and LDA (0.921). Our proposed methods produce more unbiased and precise results than the existing models, and have a great potential to produce accurate general (e.g., predicting the entire landslide) and detailed (e.g., combining the prediction of the landslide source area with the landslide run-out modeling) landslide susceptibility assessments, which can be further applied to landslide hazard and risk assessments. [ABSTRACT FROM AUTHOR]

The study of landslide dynamic characteristics and accumulation patterns is of great significance for the quantitative evaluation of landslide hazards. This paper takes the Tangjiashan landslide as a research case, a real 3D model was established based on high precision DEM data to determinate the terrain characteristics and pre- and postlandslide image data to determinate the shape and size of the sliding body. Particle flow 3D program (PFC3D) is carried out to reappear the complete movement of the Tangjiashan landslide. And the dynamic characteristic factors are monitored and analyzed such as the velocity and displacement of the landslide body. The result shows that the Tangjiashan landslide is a traction-type landslide, which lasts about 37 s in total and can be divided into four stages: destabilization and destruction, collapse and accelerated sliding, high-speed sliding, and deceleration and accumulation. The maximum sliding distance is about 642.05 m and the maximum sliding speed is about 38.67 m/s. This is a typical high-speed short-range landslide, and the speed of sliding body surface is much greater than that of other parts, which show an obvious characteristic of clastic flow. In the Tangjiashan landslide, the slip distance is the controlling factor in determining the speed of the landslide at different locations. [ABSTRACT FROM AUTHOR]

Rainfall-induced shallow landslides are widespread throughout the world, and vegetation is frequently utilized to control them. However, in recent years, shallow landslides have continued to frequently occur during the rainy season on the vegetated slopes of the Loess Plateau in China. To better probe this phenomenon, we considered vegetation cover in the sensitivity analysis of landslide hazards and used the transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model to quantitatively describe the impacts of different types of vegetation cover on slope stability. Based on the rainfall information for landslide events, the spatiotemporal distributions of the pore water pressure and the factor of safety of the vegetated slopes were inverted under the driving changes in the soil properties under different vegetation types, and the average prediction accuracy reached 79.88%. It was found that there was a strong positive correlation between the cumulative precipitation and the proportion of landslide-prone areas in woodland covered by tall trees, grassland covered by shrubs and grasses, and cultivated land. The highest landslide susceptibility, which has the greatest potential to hasten the occurrence of rainfall-induced landslides, is found in woodland with tall trees. Therefore, this paper proposes the promoting relationship between vegetation and landslide erosion, which provides a new scientific perspective on watershed management to prevent shallow landslide disasters and manage and develop watershed vegetation. [ABSTRACT FROM AUTHOR]

Mr. Peternel, the municipal engineer, I did not i mention the prehistoric landslide in his 1973 memorandum because he had no knowledge of this landslide. As presented in considerable detail in the paper, the municipality I did not cause i the subject landslide and the homeowner was unable to prove in court that the municipality caused this landslide. 2 was plotted in 2006 by the writer from the map of Davies ([1]) along with observations on 1937-1986 aerial photographs and in the field; the 1973 landslide described by then municipal engineer Arthur Peternel and observed by the writer on 1973 aerial photographs corresponded to the portion of the recent landslide behind Houses 4347, 4353, and 4359; and this 1973 landslide grew from 1973 to 2005 to extend behind House 4363, as shown in the outline of the recent landslide in Fig. As noted previously and stated in the paper, the 1973 landslide described by Mr. Peternel and observed by the writer on the 1973 aerial photographs corresponded to the portion of the recent landslide behind Houses 4347, 4353, and 4359, and this 1973 landslide grew from 1973 to 2005 to extend behind House 4363 as shown in the outline of the recent landslide in Fig. [Extracted from the article]

Current scholars have made more efforts to investigate the deformation of landslide caused by tunnel excavation, but there are few studies on the mechanical properties of existing tunnels subjected to influences of landslides. Furthermore, very little attention is paid to the simplified analytical solution for landslide-tunnel interaction and current studies still remain on the monitoring and numerical modeling of engineering. A simplified analytical method is proposed in this paper to evaluate on the deformation of existing tunnels induced by landslides. Firstly, the landslide thrust sliding forces are calculated based on the transferring coefficient method and the limit equilibrium method. Secondly, imposing the thrust sliding force on the existing tunnels, the elastic foundation beam model is established for predicting the landslide-tunnel interaction. Then, the internal force and displacement for the existing tunnels caused by the landslides are solved by the transfer matrix method. In addition, the three-dimensional numerical simulation results are compared with the simplified analytical solution and good consistency is obtained. Furthermore, the parametric analysis for influences of the cohesive force, the internal friction angle, and the unit weight of landslide body is conducted on the thrust sliding force. The influences of the thrust sliding force, the foundation coefficient of landslide and sliding bed, the tunnel lining stiffness, and the tunnel length on the existing tunnels are also analyzed with the parametric analyses by the simplified analytical solution. The paper contributes to the understanding of interaction mechanics for landslides as a potential risk in preliminary design of tunnels. [ABSTRACT FROM AUTHOR]

As natural backwater structures, landslide dams both threaten downstream human settlement or infrastructure and contain abundant hydro-energy and tourism resources, so research on their development feasibility is of great significance for permanently remedying them and effectively turning disasters into benefits. Through an analysis of the factors influencing landslide dam development and utilization, an index system (consisting of target, rule, and index layers) for evaluating development feasibility was constructed in this paper. Considering uncertainty and randomness in development feasibility evaluation, a cloud model-improved evaluation method was proposed to determine membership and score clouds based on the uncertainty reasoning of cloud model, and a cloud model-improved analytic hierarchy process (AHP-Cloud Model) was introduced to obtain weights. Final evaluation results were obtained using a hierarchical weighted summary. The improved method was applied to evaluate the Hongshiyan and Tangjiashan landslide dams and the results were compared with the maximum membership principle results. The results showed that the cloud model depicted the fuzziness and uncertainty in the evaluation process. The improved method proposed in this paper overcame the loss of fuzziness in the maximum membership principle evaluation results, and was capable of more directly presenting evaluation results. The development feasibility of the Hongshiyan landslide dam was relatively high, while that of the Tangjiashan landslide dam was relatively low. As suggested by these results, the evaluation model proposed in this paper has great significance for preparing a long-term management scheme for landslide dams. [ABSTRACT FROM AUTHOR]

A landslide inventory representing landslide locations is used as a key factor in landslide susceptibility assessment. This paper explores Google Earth (GE) for generating a polygon-based landslide inventory in Bandung Basin. How far GE can identify landslides and their boundaries, source areas, and types were discussed here. Visual interpretation of GE images supported by path tool in GE, official landslide reports, previous research papers, and media was performed. The result is a polygon-based landslide inventory consisting of 194 landslide areas and 194 landslide source areas during 1993-2020. The limitations of GE in preparing the landslide inventory are (1) not covering the timing of the landslide occurrences, (2) tricky to identify small landslides (<100 m2) in anthropogenically transformed areas, and (3) not able to distinguish between earth and debris of landslide material. [ABSTRACT FROM AUTHOR]

The rock mass along the Jinsha River is relatively broken under complex geological action. Many ancient landslides were distributed along the Jinsha River in Gongjue County, which is very dangerous under the action of gravity, tectonic stress and river erosion. Efficient and accurate identification and monitoring of landslides is important for disaster monitoring and early warning. Interferometric synthetic aperture radar (InSAR) technology has been proved to be an effective technology for landslide hazard identification and mapping. However, great uncertainty inevitably exists due to the single deformation observation method, resulting in wrong judgment during the process of landslide detection. Therefore, to address the uncertainties arising from single observations, a cross-comparison method is put forward using SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterers InSAR) technology. Comparative analysis of the spatial complementarity of interference points and temporal deformation refined the deformation characteristics and verified the reliability of the InSAR results, aiding in the comprehensive identification and further mapping of landslides. Landslides along the Jinsha River in Gongjue County were studied in this paper. Firstly, 14 landslides with a total area of 20 km2 were identified by using two time-series InSAR methods. Then, the deformation characteristics of these landslides were validated by UAV (unmanned aerial vehicle) images, multiresource remote sensing data and field investigation. Further, the precipitation data were introduced to analyze the temporal deformation characteristics of two large landslides. Lastly, the influence of fault activity on landslide formation is further discussed. Our results demonstrate that the cross-comparison of the time-series InSAR method can effectively verify the accuracy of landslide identification. [ABSTRACT FROM AUTHOR]

This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, "Physical modeling of landslide remediation constructions' behavior under static and seismic actions", and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models. [ABSTRACT FROM AUTHOR]

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after correction were compared. Moreover, the correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides. [ABSTRACT FROM AUTHOR]

Co-seismic landslides can cause devastating consequences in seismically active mountainous regions. Rapid assessment of co-seismic landslides is essential in effective emergency response. A dilemma that hinders the assessment is the presence of a significant bias, either overestimating or underestimating the actual landslide hazard. To overcome this dilemma, this paper proposes a two-phase strategy that involves constructing a hierarchical Bayesian model augmenting data from a new earthquake with historical co-seismic landslide inventories. Our findings demonstrate that even with limited and evenly distributed co-seismic data from the new earthquake, it is possible to achieve an unbiased and accurate assessment. The two-phase strategy can facilitate the rapid and unbiased assessment of co-seismic landslides, thereby informing policy-making and resource allocation. • Co-seismic landslide assessment is often biased, either over- or underestimating landslide hazard. • A two-phase strategy is proposed based on the availability of co-seismic landslide data. • A hierarchical Bayesian model is implemented in the two-phase strategy. • The two-phase strategy enables unbiased and accurate co-seismic landslide assessment. • The rapid and unbiased assessment informs emergency response and resource allocation. [ABSTRACT FROM AUTHOR]

With the rapid escalation of human activities and the exacerbation of global warming, the safety assessment of landslide dams has become increasingly crucial. Quickly and accurately evaluating their safety presents a significant challenge. This paper conducts a comparative analysis of six AI classifier models designed for assessing landslide dam stability, employing both the random forest method and the XGBClassifier method. These models are developed based on seven inputs: dam height, dam length, dam width, dam volume, dammed lake volume, catchment area, and dam material, with one output representing stability. In addition, these innovative models handle landslide dam data with missing values using zero filling or NaN (Not a Number) filling, with and without log-transformation. The stability performance of these six AI models is evaluated using metrics such as accuracy, precision, recall, and F1-score. Furthermore, data from 16 on-site landslide dams are collected and then utilized as case studies for the evaluation. Results reveal that the XGBClassifier model, integrating NaN values and coupled with log-transformation, achieves the highest classification accuracy rate at approximately 75.33 % among the six models. Additionally, it demonstrates the highest F1-score of 0.82 in predicting actually unstable dams. In the context of case studies, this model also attains an accuracy of around 68.75 %, signifying a notable improvement over traditional methods such as Blockage Index (BI), which achieves an accuracy of 31.25 %, and Dimensionless Blockage Index (DBI), which achieves an accuracy of 37.50 %. Thus, AI models exhibit significant potential for markedly enhancing the rapid prediction of landslide dam stability when compared to traditional approaches. [ABSTRACT FROM AUTHOR]

Ongoing climate change has increased the impact of landslides and related slope disasters on infrastructure and human lives. Microwave satellite remote sensing, particularly interferometric analysis of synthetic aperture radar (InSAR) data, is a powerful tool for routine monitoring of the displacement of slopes on small wavelength scales, independently from solar illumination and cloud coverage. Although various sophisticated techniques for time-series InSAR have been developed, practical application in conjunction with geological analysis to reveal intrinsic and triggering factors of slope displacement is still limited. In this paper, we describe a practical case of time-series InSAR analysis with a special focus on its geological implications in northwestern Kyushu, a high-risk area for slope-related disasters in Japan. The extracted susceptible polygons from the InSAR results show accuracy comparable with that of other recent research on landslide mapping. Intrinsic factor analysis based on a geographic information system reveals that the displacement occurs on relatively gentle slopes instead of steep areas, corresponding to the transient zone of Paleogene-Neogene sedimentary rocks and basalt. Triggering factor analysis based on correlation coefficients reveals a significant link between some displacement areas with mean and/or maximum precipitation for each observation duration. Those findings confirm the importance of carefully accounting for the geological background for landslide susceptibility assessment and policy making, apart from topographic and meteorological conditions. [ABSTRACT FROM AUTHOR]

• MLP, GRU, CNN and MSCNN for landslide susceptibility mapping were compared. • CNN combined with multi-scale technique can improve feature utilization. • The joint evaluation method of ROC curve and PR curve for LSM was proposed. Landslide susceptibility mapping (LSM) can be used to determine the spatial probability of landslide occurrence. There are many methods for LSM, including statistical methods, traditional machine learning methods and deep learning methods, etc. However, the difference comparison of these methods has been not perfect, especially the comparison of different neural network models for LSM and their application prospects were rarely studied. In this paper, the classical neural net-work multi-layer perceptron (MLP), convolutional neural network (CNN), gated recurrent unit (GRU) and multi-scale convolutional neural network (MSCNN) four models are selected for comparison. Taking Lanzhou city, Gansu Province, China as an example, eight landslide-related influencing factors and historical landslide and non-landslide locations were selected, and the training set and validation set were divided according to 7:3. Through training the four models, four landslide susceptibility maps were generated. The experimental results were verified and compared by the confusion matrix, Kappa coefficient, F1-score and other statistical indicators. The receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve were plotted to evaluate the classification effect and generalization capability of four models. The results show that the constructed MSCNN is the optimal model, which has the best performance both in the training process and in the mapping results. MSCNN model has the highest value of Recall (99.93%), Kappa (0.96) and F1-score (0.98) in the confusion matrix. In addition, ROC curve and PR curve of MSCNN model maintain the maximum area under curve (AUC) on different data sets. In the comparison, MLP and GRU accept sequence features, while CNN and MSCNN accept neighborhood features. In general, the prediction model considering neighborhood features contains more information in the limited input data and is better than the prediction model considering sequence features in all evaluation indicators. Therefore, we think that the neighborhood features can better represent the landslide occurrence characteristics. In the future model design process for LSM, more attention should be paid to the neighborhood features of landslide influencing factors. [ABSTRACT FROM AUTHOR]

After the "5·12" Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood disasters. Therefore, it is of great significance to build a collapse and landslide susceptibility evaluation model in Wenchuan County for local disaster prevention and mitigation. Taking Wenchuan County as the research object and according to the data of 1081 historical collapse and landslide disaster points, as well as the natural environment, this paper first selects six categories of environmental factors (13 environmental factors in total) including topography (slope, aspect, curvature, terrain relief, TWI), geological structure (lithology, soil type, distance to fault), meteorology and hydrology (rainfall, distance to river), seismic impact (PGA), ecological impact (NDVI), and impact of human activity (land use). It then builds three single models (LR, SVM, RF) and three CF-based hybrid models (CF-LR, CF-SVM, CF-RF), and makes a comparative analysis of the accuracy and reliability of the models, thereby obtaining the optimal model in the research area. Finally, this study discusses the contribution of environmental factors to the collapse and the landslide susceptibility prediction of the optimal model. The research results show that (1) the areas prone to extremely high collapse and landslide predicted by the six models (LR, CF-LR, SVM, CF-SVM, RF and CF-RF) have an area of 730.595 km2, 377.521 km2, 361.772 km2, 372.979 km2, 318.631 km2, and 306.51 km2, respectively, and the frequency ratio precision of collapses and landslides is 0.916, 0.938, 0.955, 0.956, 0.972, and 0.984, respectively; (2) the ranking of the comprehensive index based on the confusion matrix is CF-RF>RF>CF-SVM>CF-LR>SVM>LR and the ranking of the AUC value is CF-RF>RF>CF-SVM>CF-LR>SVM>LR. To a certain extent, the coupling models can improve precision more over the single models. The CF-RF model ranks the highest in all indexes, with a POA value of 257.046 and an AUC value of 0.946; (3) rainfall, soil type, and distance to river are the three most important environmental factors, accounting for 24.216%, 22.309%, and 11.41%, respectively. Therefore, it is necessary to strengthen the monitoring of mountains and rock masses close to rivers in case of rainstorms in Wenchuan county and other similar areas prone to post-earthquake landslides. [ABSTRACT FROM AUTHOR]

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented. [ABSTRACT FROM AUTHOR]

The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region. However, there are uncertainties in machine learning applications. In this paper, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are used to assess the landslide susceptibility in order to discuss the model uncertainty. The model uncertainty is explained in three ways: landslide susceptibility zoning result, risk area (high and extremely high) statistics, and the area under Receiver Operating Characteristic Curve (ROC). The findings indicate that: (1) Landslides are restricted by influence factors and have the distribution law of relatively concentrated and strip-shaped distribution in space. (2) The percentage of real landslide in risk area is 86%, 87%, 82%, and 61% in SVM, RF, LR, and ANN, respectively. The area under ROC of RF, SVM, LR, and ANN, respectively, is 90.92%, 80.45%, 73.75%, and 71.95%. (3) Compared with the prediction accuracy of the training set and test set from the same earthquake, the accuracy of landslide prediction in the different earthquakes is reduced. [ABSTRACT FROM AUTHOR]

This paper introduces the Landslide Susceptibility Assessment Tools – Project Manager Suite (LSAT PM), an open-source, easy-to-use software written in Python. Primarily developed to conduct landslide susceptibility analysis (LSA), it is not limited to this issue and applies to any other research dealing with supervised spatial binary classification. LSAT PM provides efficient interactive data management supported by handy tools in a standardized project framework. The application utilizes open standard data formats, ensuring data transferability to all geographic information systems. LSAT PM has a modular structure that allows extending the existing toolkit by additional tools. The LSAT PM v1.0.0b implements heuristic and data-driven methods: analytical hierarchy process, weights of evidence, logistic regression, and artificial neural networks. The software was developed and tested over the years in different projects dealing with landslide susceptibility assessment. The emphasis on model uncertainties and statistical model evaluation makes the software a practical modeling tool to explore and evaluate different native and foreign LSA models. The software distribution package includes comprehensive documentation. A dataset for testing purposes of the software is available. LSAT PM is subject to continuous further development. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of the Vögelsberg landslide located in the municipality of Wattens (Tyrol, Austria), which reactivated in 2016, causing damages to nearby buildings and infrastructures. Since the date of reactivation, a modern monitoring system has been implemented with the installation of in-situ geodetic automated tracking total stations (ATTS), an inclinometer and two piezometers. Here, we describe two distinctive methods, the Breaks for Additive Seasonal and Trend (BFAST) and the Vector Inclination Method (VIM) used to characterize the landslide from the kinematic and geometrical point of view. The main input data, used for both methods, derive from processing a stack of several Sentinel-1 differential interferograms with the Multiple Small Baseline Subset (MSBAS) 2D and 3D algorithms. BFAST allowed highlighting the seasonality of the phenomenon from the analysis of the time series as well as the trend and the breakpoints that identify the landslide reactivation phases. These latter were then correlated with the main triggering factors such as rain and snow melting. The application of the VIM through the exploitation of the MSBAS displacement vectors allowed the reconstruction of the depth of the landslide slip surface along both the longitudinal and transversal direction and, in turn, the evaluation of the volumes of material mobilized by the landslide. The results obtained further prove that procedures for the in-depth analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data can contribute to slow-moving landslide characterization, which represents a fundamental step for landslide hazard assessment within quantitative risk analyses. [ABSTRACT FROM AUTHOR]