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Strong earthquake geological hazards cause significant social and economic losses. The assessment of post-earthquake socioeconomic development levels is one of the important bases from which to measure the recovery capacity of hazard areas. However, the long-term impact of geological hazards is rarely considered in the assessment of the socioeconomic development level of a mountainous earthquake-stricken area. The purpose of this paper is to study the complex relationship between earthquake geological hazard effects and socioeconomic development in the long-term post-earthquake development and reconstruction of mountainous extremely earthquake-stricken areas, to provide a reference for the study area to achieve regional sustainable development goals and high-quality development. On this basis, using the economic, social, ecological environment and other relevant data from 2008 to 2016, and using unsupervised machine learning algorithms, a socioeconomic development level evaluation model based on spectral clustering was established, and the effects of different kernel function scale parameters on the model were analyzed. The optimal parameters were determined, and the spatiotemporal analysis of the socioeconomic development level of the study area was carried out. The research results show that the performance of the evaluation model is optimal when the output category is 4 and the scale parameter is 0.26, and the scale parameter of the kernel function is an important indicator that affects the accuracy of the model. From 2008 to 2016, the socioeconomic development level of Qingchuan has always been at a very low level, Wenchuan and Beichuan have always been at a low level, Shifang and Mianzhu have always been at a high level, and Pengzhou and Dujiangyan have always been at a very high level. The socioeconomic development level of Anxian, Maoxian and Pingwu fluctuates greatly over time. Another interesting finding is that the socioeconomic development level of the mountainous earthquake-stricken area has a strong correlation with its own industrial structure and landslide hazard effects. The work done in this paper is of great significance for understanding the temporal and spatial effects of hazards and understanding the complexities of regional disaster systems. [ABSTRACT FROM AUTHOR]

In order to mitigate/prevent the risks of landslides, one of the essential tools that can be used to manage and plan the development of human settlements is landslide susceptibility. The two metaheuristic algorithms explored in this paper are the SCE and VSA algorithms used to optimize the artificial neural network (ANN) model. By integrating the two algorithms with the artificial neural network model, we try to determine its optimum computational parameters in order to generate the landslide susceptibility mapping for the Kurdistan province in Iran. Sixteen causative factors of landslides are included in the spatial database. The landslide susceptibility maps were generated in a GIS medium, and in order to evaluate the employed predictive models, the criterion of the area under the curve (AUC) was employed. This investigation includes 1072 landslide events, which are divided as follows: one-third as testing data and two-thirds as training data (i.e., a 75:25 ratio). The results indicate that after using the abovementioned algorithms, the AUC increased noticeably from 0.708 to 0.788 for SCE-MLP and from 0.744 to 0.818 for VSA-MLP in the training phase. The criterion of the area under the curve was utilized in order to evaluate the accuracy of the employed probabilistic models. Incidentally, the comparable AUCs calculated for the VSA-MLP testing databases and the obtained AUCs were 0.818, 0.801, 0.791, 0.786, 0.784, 0.778, 0.777, 0.776, 0.754 and 0.744, respectively for population size in training databases equal to 50, 350, 500, 150, 450, 200, 400, 300, 100 to and 250. Also, in case of SCE-MLP, the training and testing AUC were found (0.878, 0.865, 0.851, 0.850, 0.819, 0.816, 0.815, 0.781, 0.760, and 0.756) and (0.788, 0.782, 0.759, 0.744, 0.727, 0.720, 0.718, 0.713 and 0.708). The best fit for swarm size conditions of the SCE-MLP and VSA-MLP model showed 150 and 350, respectively. The acquired results indicate that the VSA-ANN model has a better predictive capability in optimization of the structure of the artificial neural network model and computational parameters in comparison with the SCE-ANN. [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]

Landslides are among the most critical geo-environmental disasters for both humans and the environment. This study intended to create a landslide susceptibility map (LSM) for the Akre District of Kurdistan Region, Northern Iraq. In this paper, 15 landslide causative criteria including elevation, slope, curvature, aspect, topographic wetness index, topographic roughness index, steam power index, lithology, lineament density, soil types, land use/cover, normalized difference vegetation index, distance to roads, rainfall, and distance to streams were analysed. For this purpose, frequency ratio (FR), analytic hierarchy process (AHP), and ensemble FR-AHP models based on geoinformatics techniques were applied. The LSMs were then assigned into five categories based on their susceptibility levels: very low, low, medium, high, and very high. The results showed that the high and very high landslide susceptibility categories for the FR, AHP, and hybrid FR-AHP models were 27.75% (732,01 km2), 28.44% (750,36 km2), and 28.84% (761 km2), respectively. The results showed that the majority of historical landslide incidents occurred in mountainous terrain in the northern parts of the study area, which are classified as high and very high susceptibility zones. The predicted rate curves for the FR, AHP, and hybrid FR-AHP models had areas under curve of the receiver operating characteristic curve (AUC-ROC) values of 93.4%, 89.3%, and 93.8%, respectively, which indicate that the ensemble FR-AHP model provides more reliable and accurate results for LSM. The LSM generated via the hybrid FR-AHP model can be utilised by local authorities, managers, and decision-makers in further land use/cove planning to mitigate the devastating influences of landslides in the area. [ABSTRACT FROM AUTHOR]

During the last decades, natural hazards, such as earthquakes, volcanic eruptions, landslides, floods, storms, droughts, and cyclones, have increased in frequency and severity, influenced by climate change and population growth. These natural hazards can become sudden-onset disasters, causing human losses and property damages that affect economic stability and growth. Although there is a pressing need for risk decrease and adaptation strategies to these unexpected events, targeted natural hazard insurance would be a valuable tool to counteract governmental and individual consequences. This paper summarizes an overview of risk assessment and mitigation strategies based on geological, geomorphological, and meteorological factors. The study includes an examination of monitoring systems for movement and gas emissions, risk and emergency maps, and highlighting the vulnerability of different areas at national and regional levels. Furthermore, the paper addresses the importance of promoting comprehensive geological and geotechnical knowledge among citizens of every socio-economical group and proposing the tools to effectively deliver the message, aiming at increasing the willingness for natural hazard insurance at both individual and governmental scales for human and property protection. [ABSTRACT FROM AUTHOR]

Background: The Cameroon Volcanic Line (CVL) is an oceanic-continental megastructure prone to geo-hazards, including landslide/mudslide, gully erosion and flash floods targeted in this paper. Recent geospatial practices advocated a multi-hazard analysis approach supported by artificial intelligence. This study proposes the Multi-Geoenvironmental Hazards Susceptibility (MGHS) tool, by combining Analytical Hierarchy Process (AHP) with Machine Learning (ML) over the North-Moungo perimeter (Littoral Region, Cameroon). Methods: Twenty-four factors were constructed from satellite imagery, global geodatabase and fieldwork data. Multicollinearity among these factors was quantified using the tolerance coefficient (TOL) and variance inflation factor (VIF). The AHP coefficients were used to weigh the factors and produce a preliminary map per Geoenvironmental hazard through weighted linear combination (WLC). The sampling was conducted based on events records and analyst knowledge to proceed with classification using Google Earth Engine (GEE) cloud computing interface. Classification and Regression Trees (CART), Random Forest (RF) and Gradient Boosting Regression Trees (GBRT), were used as basic learners of the stacked hazard factors, whereas, Support Vector Regression (SVR), was used for a meta-learning. Results: The rainfall was ranked as the highest triggering factor for all Geoenvironmental hazards according to AHP, with a coefficient of 1, while the after-learning importance assessment was more varied. The area under receiver operating characteristic (AUROC/AUC) was always more than 0.96, and F1-score is between [0.86–0.88] for basic classifiers. Landslides, gully erosion and flash floods showed different spatial distributions, confirming then their probability of co-occurrence. MGHS outputs clearly displayed two and three simultaneous occurrences. Finally, the human vulnerability assessed with population layer and SVR outputs showed that high human concentrations are also the most exposed, using the example of Nkongsamba's extract. Conclusions: Combining AHP with single learners, then a meta-learner, was efficient in modelling MGHS and related human vulnerability. Interactions among geo-environmental hazards are the next step and city councils are recommended to integrate results in the planning process. [ABSTRACT FROM AUTHOR]

The paper presents the case study of the recurrent slope movement in colluvium deposits at Doi Chang, Thailand. A thorough site investigation confirmed the slope movement rate corresponding to slow creep during dry season, while in the rainy season, its velocity remarkably increased. Despite frequent repair, the movement rate was sufficient to result in the recurrent damage of infrastructures like roads and buildings, causing economic loss and public concerns. Furthermore, surface mapping revealed that the hill's topography led to the concentration of flowing water in a particular area. This resulted in a high level of groundwater table, especially during the rainy season. The inclinometer installed in that area suggested an average movement rate of 20.5 mm/month in the wet season. In contrast, during the dry season, it was limited within 2 mm/month, indicating that the increase in the rate of slope movement in the colluvium deposit was primarily due to the rising groundwater table. Field and laboratory tests were conducted to determine the properties of the colluvium deposit. Landslide susceptibility assessment was performed using infinite slope model and later integrated with GIS to evaluate the factor of safety (FS) over a large area. The FS decreased below 1 when the groundwater level rose to 0.3 cm below the ground surface, and using GIS, based on infinite slope model, the potential risk zone were delineated. [ABSTRACT FROM AUTHOR]

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]

Understanding the temporal and spatial patterns of flood in the Awash River basin, which is located in Ethiopia's Afar region, is crucial. The Awash basin was picked because it is continuously in danger both spatially and temporally. The likelihood of flooding was assessed using eight independent variables: elevation, slope, rainfall, drainage density, land use, soil type, wetness index, and lineament density. Each constituent was assigned a weight based on its susceptibility to the danger, which was classified into four classifications. Exploratory regression analysis showed that the existing land use is the main factor influencing flood susceptibility. For the GIS domain, a total of 31 models were built using exploratory regression. Model number 31 was found to be the best fit model, having the highest Adjusted R2 value of 0.8 and the lowest Akaike's Information criterion value of 1536.8. The spatial autocorrelation tool's Z score and p-value for the standard residuals are, respectively, 0.7 and 0.4, indicating that they were neither clustered nor scattered. The geographic breadth of flood susceptibility and risk is thoroughly examined in this paper, as is the significance of spatial planning in the Awash basin. [ABSTRACT FROM AUTHOR]

Identifying landslide-prone areas is an essential step in assessing landslide risk and reducing landslide damage. In this paper, GIS-based spatial analysis has been used to prepare the landslide susceptibility (LS) map in the north of Lorestan province in western Iran. For this purpose, three main criteria and their sub-criteria were identified as causative factors including geology and topography (i.e., distance from the fault, lithology, slope, aspect, and elevation), climate (i.e., rainfall and distance from the river), and environmental parameters (i.e., distance from the road, land-cover, NDVI). One hundred thirty-six known landslides were randomly divided into training (≅ 70%) and validation (≅ 30%) datasets. This study is based on the integration of popular analytic hierarchy process (AHP), frequency ratio (FR), and the fuzzy gamma operator (FGO) techniques. AHP was utilized to prioritize causal factors and fuzzy technique was applied in two stages of factor map fuzzification and calculation of sub-criteria maps and then overlap of fuzzified map layers. The fuzzy membership (FM) values were determined based on the FR method, which was normalized between the ranges of 0 and 1. Finally, LS zoning maps were estimated in five susceptibility classes (very low, low, moderate, high, and very high). Validation processes by comparing the three output maps with the layer of validation landslides in the study area and area under receiver operating characteristic curve confirm that the gamma value of 0.9 (AUC = 0.88) offers a more accurate LS map compared to other gamma values. The results of this study will be reliable for landslide risk reduction strategies. [ABSTRACT FROM AUTHOR]

Natural dams formed by landslides may produce disastrous floods after dam outbursts. How the breaching characteristics of natural dams are affected by initial soil moisture has remained insufficiently understood. In this paper, we present the results of a series of laboratory tests that assessed five different initial soil moistures (0.3, 2.4, 4.2, 7.3, and 10.3%). Under the present experimental conditions with dams composed of gravel–sand–clay mixtures, the failure of natural dams was primarily caused by the erosion of overtopping flow, and lateral mass collapse also caused breach widening. According to the test results, three stages of the breaching process of natural dams made of different initial soil moistures were observed. The results show that peak discharge increased with the increase in the initial soil moisture, while the breaching time and height of the residual dam decreased. In the process of the breach, the backward erosion was weakened gradually with the increase in the initial soil moisture. When the initial soil moisture increased, the breach deepened faster than it widened, and the ratio of the breaching width to depth after dam outburst tended to be greater than 1 at first and then less than 1. A function of the breaching width and depth is established, making it possible to calculate both variables. This function is based on a shape parameter that linearly decreases with initial soil moisture. [ABSTRACT FROM AUTHOR]

Mapping susceptibility of landslide disaster is essential in subtropical area, where abundant rainfall may trigger landslide and mudflow, causing damages to human society. The purpose of this paper is to propose an integrated methodology to achieve such a mapping work with improved prediction results using hybrid modeling taking Chongren, Jiangxi as an example. The methodology is composed of the optimal discretization of the continuous geo-environmental factors based on entropy, weight of evidence (WoE) calculation and application of the known machine learning (ML) models, e.g., Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The results show the effectiveness of the proposed hybrid modeling for landslide hazard mapping in which the prediction accuracy vs the validation set reach 82.35–91.02% with an AUC [area under the receiver operating characteristic (ROC) curve] of 0.912–0.970. The RF algorithm performs best among the observed three ML algorithms and WoE-based RF modeling will be recommended for the similar landslide risk prediction elsewhere. We believe that our research can provide an operational reference for predicting the landslide hazard in the subtropical area and serve for disaster reduction and prevention action of the local governments. [ABSTRACT FROM AUTHOR]

Fire is one of the main causes of environmental and ecosystem change. Geospatial data, derived from satellite images and surveying observations, are a useful tool in managing land use and land cover changes. In this paper, we present a multi-criteria-based geographical information system (GIS) for fire risk assessment and fire potential mapping in a peat swamp forest at Hua Sai district, Thailand. Fifty-five fire points in peat swamp areas were reported from 2012 to 2016. Analytic hierarchy process (AHP) and GIS methods were used synergistically to analyze the following contributing factors: elevation, slope, aspect, precipitation, distance from river, distance from settlement and land use. The results of the present study indicate that the predicted fire risk areas from the methodology proposed are found to be in agreement with recorded past fire events. The fire risk map produced can be used for planning and management of wildland fire events in the future. GIS multi-criteria-based models have been developed in the context of fire prognosis; however, most of them attribute weights from simple pair-wise comparisons; we showcase that the integration of AHP provides accurate results for this study area in Thailand. [ABSTRACT FROM AUTHOR]

A permanent displacement model (PDM) is often used for hazard assessment of earthquake-induced landslides (ELHA) at regional scale. Pulse-like ground motions (PLGM) are rarely considered in the existing PDMs, although they have been proved to have significant influence on the initiation of landslides in near-fault area. In this paper, taking an area affected by 1994 Mw 6.7 Northridge earthquake as an example, 3 PDMs composed of PLGM, and 11 other models were selected to compare and verify the effects of PLGM. According to the results of multiple evaluation metrics such as the ground failures captured (%GFC), receiver operating characteristics (ROC), and quality sum (Qs), it indicates that the PDMs composed of PLGM use less ground motion records, but the accuracy of evaluation results is higher. This shows that PLGM have significant effect on ELHA. The ELHA method considering the near-fault PLGM was applied to a certain area along the Sichuan-Tibet Railway. The results show that the displacement model considering PLGM predicted more landslides cells. Among them, 8.45% of the slopes are in the extremely high hazard area, and about 30% of the extremely high hazard area is within 5 km of the railway. The slope with high hazard of earthquake-induced landslide can be investigated and protected in advance to avoid casualties and property losses. [ABSTRACT FROM AUTHOR]

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

Landslide phenomenon in accumulated erodible bed sediments in a reservoir is one of the issues in hydraulic and sedimentation sciences that has received little attention. We intend to model two-dimensional changes of the water surface in a reservoir and of an erodible bed caused by a non-rigid landslide using a particle-based meshless approach. In this study, a fully explicit three-step algorithm is used. In this method, approximate numerical solution to the equations of the fluid dynamics is obtained by replacing the fluid with a set of particles. The governing equations for water flow and sand mass movement are solved for each particle. The movement of each particle, which is in interaction with other particles, is tracked. Experiments of a dam break on a dry bed, and submarine rigid and non-rigid landslides have been used to validate the method. Results indicate that the model was successfully calibrated against the measured data. Moreover, good agreement with the measured data demonstrates high capabilities of this method in simulating free-surface flows and wave-related phenomena. After the model validation, changes of erodible bed in a reservoir due to a non-rigid landslide were modelled. In this study, non-rigid landslide masses and sediment materials were modelled by non-Newtonian Carreau-Yasuda fluid, which is the novelty in the analysis of this type of natural hazard. Two possible scenarios were analyzed—one with the sliding material lighter, and the other with the sliding material heavier than the deposited sediments. The model was run until the landslide completely collapsed and its full impact was applied to the reservoir bed sediments. Additionally, we waited until the water level reached a steady state. These examples demonstrate that the model presented in this paper can be used as a reliable tool for modelling these phenomena. [ABSTRACT FROM AUTHOR]

The Loess Plateau can be considered as a landslide-prone area in northwest China. The genera consensus about the interaction between landslide deposit and terrace sediments is not well studied; this paper summarised 40 loess landslides in the South Jingyang Platform, Shaanxi Province, China to help understand of this issue. Four of the loess landslides with high mobility have been analysed in detail. Three trenches T1, T2 and T3 dug after the loess landslides LD37, LD11 and LD38 highlighted the landslide-induced changes in geomorphology and internal geometry of geology, respectively. Furthermore, observation of upwards seepage flow on the profile of trench T3 is believed to be the trigger of the high speed, and long runout flowslides in the study area. A newly developed sandbox apparatus is used to reproduce the landslide kinematics due to a mass travelling over an inclined plane. The sandbox experiments show that the sediments are sheared and pushed upwards after the collision with the deposits. The deposits are then wrapped in a space between sediments, which tends to form the 'sandwich' structure. The distal sediments are thrust when the loess deposits' kinetic energy consistently dissipates, developing the accumulated folded strata. These results reveal the deposits' interactions with the sediments in the study area and provide key guideposts regarding prevention and mitigation of loess landslide hazards. [ABSTRACT FROM AUTHOR]

Landslide deformation characterized with step-like curves often presents periodicity implicitly. This paper proposed a novel data-driven approach that adopted periodic neural network (PNN) and variational mode decomposition (VMD) to conduct displacement prediction based on the intrinsic seasonality of step-like landslide displacement. PNN was a novel neural network designed for capturing the seasonality of the time series. Firstly, the initial displacement would be decomposed into trend component, periodic component, and random component using the variational mode decomposition (VMD). Then, the external triggering factors were also decomposed by VMD into several subsequences. Subsequences with periodic and random characteristics were selected as the input datasets to forecast the periodic and random components by PNN. Finally, the total displacement was obtained by superimposing the three predictive components to validate the model performance. The Baishuihe landslide was taken as a case study to validate the high effectiveness and efficiency of our method. The result proved that our new model presented satisfactory prediction accuracy without complex training process. Meanwhile, PNN performed a strong robustness to the missing values due to the advantage of its structure. In addition, we clarified a corrective data processing mode as "strict" mode: the dataset has to be divided into training and validation sets firstly to avoid the leakage of the future data. [ABSTRACT FROM AUTHOR]

The Southern Carpathians (Romania) are one of the highest, steepest and most massive sectors of the Carpathian Mountains, with a strong imprint of the Pleistocene glaciation, which are widely affected by a broad spectrum of natural hazards (avalanches, rock wall weathering and landslides, flash floods). This paper focuses on the detection of recent changes in annual temperature and precipitation extremes over the 1961–2018 period based on observational data, discussing their spatial distribution and dependencies (on altitude, latitude and longitude). Trend analysis was applied on a set of 16 indices selected from the core indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results show a significant warming trend, well reflected by the strong decline of frost days and icing days and a significant increase in absolute maximum temperatures, summer days and warm spell duration. Trends in precipitation extremes are more various, suggesting a climate drying trend, apparently stronger in the areas with a fast rise of maximum temperatures (evidence of coupled warming-drying climate change signal) and an increasing frequency of heavy precipitation events and resulted water amounts, in the areas affected by summer convection intensification. Except TNn with trends slopes revealing a winter nighttime amplification with elevation, high elevations (at above 2000 m) show only slight changes in temperature and precipitation extremes compared to the lower areas, explaining the general lack of elevation dependency of trends. Longitude explains 48 to 54% of trends in heavy precipitation indices. Changes in the large-scale atmospheric circulation (e.g. intensification of westerly circulation, increasing frequency of blocking conditions), cloudiness and summer convection have been identified among the main factors influencing trend behaviour and their spatial distribution. [ABSTRACT FROM AUTHOR]

The study of landslide initiation mechanisms and dynamic process analysis is important for monitoring, predicting, and controlling landslide occurrence. A giant landslide that occurred on July 19, 2018, in Yanyuan County, Southwest China, was investigated, providing a research example. In this paper, the failure mechanism and dynamic process of the Boli landslide were analyzed using field investigations, laboratory experiments, unmanned aerial vehicle (UAV) photogrammetry, remote sensing images, hydrological calculations, and numerical simulations. The results indicated that the antecedent rainfall was not the direct factor to saturate the sliding mass. The runoff supply provided by the comb-like channel group upstream of the landslide reached 709.5 mm, which was an important hydrodynamic triggering point for accelerating the sliding mass to saturation and instability. Numerical simulation results showed that the evolution of the landslide included three stages: low-speed initiation, accelerating sliding, and flow deposition. The maximum sliding speed of the landslide occurred during the second stage and reached 39 m/s. From the source zone to the Taozi Gully, the silt content decreased from 67.9 to 40%, which was an internal particle–triggering factor of the alteration of the sliding mass from the liquefaction-resistant state to the liquefaction-prone state. The study has revealed the failure mechanism of the landslide and presented its dynamic process, which could be used to provide a reference for the study of similar landslides mainly induced by runoff supplied by such channel groups. [ABSTRACT FROM AUTHOR]

Rainfall is one of the most significant natural factors for inducing landslides. The shallow saturated zone formed in slopes during rainfall is an important factor in inducing shallow landslides. In this paper, the characteristics and influencing factors of the saturated zone in soil slopes, and the relationship between the soil surface permeability coefficient and the rainfall intensity are analyzed. In this research, the equation that characterizes the rainfall intensity by the soil saturation is built based on the Mualem-van Genuchten model, which mathematically describes the soil water characteristics of unsaturated soils. Using this model, the target saturation of the slope surface during rainfall can be determined accurately, with an average deviation of − 0.008. Based on the equations, the innovative critical curve model of the rainfall intensity−time required to form the shallow saturated zone is established and calculated. The influence of the soil material, slope angle, rainfall intensity and antecedent rainfall on the critical curve for the shallow saturated zone is analyzed. The results indicate that for slopes of 30°~40°, it is easy for the shallow saturated zone to form, and the previous rainfall after more than 16 days has little impact on the critical curve. Furthermore, an innovative landslide prediction model, based on the critical curve for the shallow saturated zone and I-D modeling, is proposed to provide benchmarks for the prediction-specific characteristics of deep or shallow landslides, which is verified by the case of the Madiwan landslide. [ABSTRACT FROM AUTHOR]

This paper aims to compare and develop the influence on different sample sizes and sample ratios when using machine learning (ML) models, i.e., support vector machine (SVM) and artificial neural network (ANN), to produce landslide susceptibility maps (LSMs) in Penang Island, Malaysia. At the same time, traditional statistical (TS) models are also considered to produce LSMs in this comparative research. The receiver operating characteristic (ROC) curve and recall metric are applied to evaluate the model's performance. Based on the evaluation criteria, the ML model outperforms the TS models and the ML models trained using the datasets with larger sample size give a better performance. ML models, especially SVM models, have better performance when training with balanced datasets as well as the datasets of more landslide sample data. Kruskal-Wallis test and Mann-Whitney U test are applied to test the significance. The results indicate that sample size and sample ratio are essential factors when considering ML models to produce LSMs. The LSMs produced in this research can provide valid and useful information to the local authorities for landslide mitigation and prediction. [ABSTRACT FROM AUTHOR]

Data mining has a significant role in hyrdrologic research. Among several methods of data mining, Bayesian network theory has great importance and wide applications as well. The drought indices are very useful tools for drought monitoring and forecasting. However, the multi-scaling nature of standardized type drought indices creates several problems in data analysis and reanalysis at regional level. This paper presents a novel framework of data mining for hydrological research—the Bayesian Integrated Regional Drought Time Scale (BIRDts). The mechanism of BIRDts gives effective and sufficient time scales by considering dependency/interdependency probabilities from Bayesian network algorithm. The resultant time scales are proposed for further investigation and research related to the hydrological process. Application of the proposed method consists of 46 meteorological stations of Pakistan. In this research, we have employed Standardized Precipitation Temperature Index (SPTI) drought index for 1-, 3-, 6-, 9-, 12-, 24-, and ()month time scales. Outcomes associated with this research show that the proposed method has rationale to aggregate time scales at regional level by configuring marginal posterior probability as weights in the selection process of effective drought time scales. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners. [ABSTRACT FROM AUTHOR]

In a strong earthquake, not only a large number of coseismic landslides are triggered, but relaxation and cracks in the rocks and soils are induced, which make these rocks and soils vulnerable to instability during subsequent rainfall; thus, strong earthquakes always have long-term effects on landslides. The geo-hazards along the Zhaotong-Qiaojia road in the 2014 Ludian earthquake (Ms 6.5) of Yunnan Province, China, were investigated, and the Ganjiazhai giant landslide was chosen as a case study. First, using the limit equilibrium analysis and Newmark method, the critical seismic intensity of the landslide before the earthquake was evaluated. Secondly, the dynamic failure process of the landslide under the measured ground motion was simulated with the discontinuous deformation analysis method. Lastly, based on stress–seepage coupling analysis and precipitation data from Ludian meteorological station, the stability of the landslide during subsequent rainfall after the earthquake was predicted. The results show that the critical seismic intensity was within degrees VIII–IX, which is consistent with the results of the earthquake damage investigation. The dynamic failure process can be divided into four stages, and four scarps formed; the potential sliding zones during the subsequent rainfall were at the first scarp and the fourth scarp, and their critical rainfall amounts were 35–40 mm and 55–60 mm, respectively. In this paper, failure process simulation and stability prediction of the landslide before, during, and after the strong earthquake are presented, which provide analysis methods for the dynamic stability of landslides in the meizoseismal area. [ABSTRACT FROM AUTHOR]

Topography is a crucial characteristic reflecting the stability status of a landslide dam. The methods for measuring landslide dam topography in a laboratory-scale test study are currently operator dependent, time-consuming, or only applicable to the measurement of the two-dimensional section. In this paper, a laboratory-scale photogrammetric method based on the structure from motion (SfM) technique was proposed to measure the three-dimensional (3D) topography of a landslide dam. The SfM technique, which is a revolutionary, low-cost, user-friendly computer vision technique, was employed for reconstructing a landslide dam 3D computer model. A scientific method to determine the topography parameters of a landslide dam was put forward. Meanwhile, two materials with different particle sizes were used to simulate the formation process of the landslide dam in the laboratory-scale. Then, the measurement results of the two materials were compared. Five parameters of a landslide dam topography with 100 parallel measurement results for each parameter were obtained. The results show that the SfM technique could build a high-quality 3D point cloud in a laboratory scale. The proposed method of determining the topography parameters of a landslide dam was useful and has low measurement uncertainty. The material type affected the sparse density of the point cloud and then affected the measurement uncertainty of landslide dam topography parameters. The measurement uncertainty of the gravel-type landslide dam was significantly higher than that of the sand-type landslide dam. This research contributes to promoting the application of a photogrammetric method based on the SfM technique in geotechnical engineering laboratory-scale tests. [ABSTRACT FROM AUTHOR]

Landslide dams are formed mostly in V-shaped valleys, with poor stability and a high risk of failure. Once landslide dams fail, they may cause floods or debris flows that endanger lives, property, and infrastructure downstream. Therefore, it is important to evaluate the stability immediately once a landslide dam is formed. The geometry of a landslide dam is the most critical to its stability assessment. However, the complex terrain and geomorphological conditions in the disaster area often make field surveys difficult. Aerial photography and satellite imagery technology are usually limited by poor weather conditions, which bring severe restrictions to the timely acquisition of the geometry of a landslide dam. In this paper, a discrete element simulation model was proposed and verified by two experiments, and then a series of numerical simulations were performed to investigate the geometry and formation process of landslide dams under different topographic factors including the motion path dip-angle, the river bed inclination, and the angle between valley slopes (valley angle). The results show that the geometry and formation process of landslide dams are significantly affected by the topographic factors. According to the results of comprehensive numerical analysis, a simple predictive model for landslide dam geometry in V-shaped valleys was established, and two landslide dams with data collected from early events were employed to validate the predictive model. Based on the easily accessible topographical factors, the proposed model accomplished the fast prediction of landslide dam geometry. This research provides a vital basis for the stability evaluation of landslide dams and can be used for the disaster emergency response before the dams break. [ABSTRACT FROM AUTHOR]

Landslide susceptibility is the likelihood of landslide occurrence, in a specific place and time. The identification of the potential relationships between landslide susceptibility and conditioning factors is very important towards landslide hazard mitigation. In this paper, we implement a local statistical analysis model geographically weighted regression, in two catchment areas located in northern Peloponnese, Greece. For this purpose, we examined the following eight conditioning factors: elevation, slope, aspect, lithology, land cover, proximity to the drainage network, proximity to the road network, and proximity to faults. Moreover, the relationship between these factors and landsliding in the study area is examined. The local statistical analysis model was also evaluated by finding its differences with the performance of a standard global statistical model logistic regression. The results indicated that the global statistical model can be enhanced by the application of a local model. The outputs of the proposed approach favored a better understanding of the factors influencing landslide occurrence and may be beneficial to local authorities and decision-makers dealing with the mitigation of landslide hazard. [ABSTRACT FROM AUTHOR]

On February 05 2016, a significant and unfortunate series of mine disasters occurred at the Lily gold mine in South Africa, where 79 mineworkers were trapped under a huge rock and soil mass. In this paper, two separate geographical information system based models are proposed; one for surface and one for subsurface mine disasters. The multi-temporal (2004–2017) high-resolution pre and post-disaster satellite images were analyzed to assess the magnitude and spatial extent of the surface disaster. The explicit modeling technique was applied for mine subsidence using the Mohr–Coulomb failure criterion under gravitational acceleration. For surface disaster, the weighted overlay model was applied by assigning weights to hydrological (rainfall, flow direction, accumulation, and density); geological (geology and lineaments) and geomorphological (slope, aspect, and curvature) causative factors. The mine subsidence modeling results showed a subsidence zone of 55 × 24 × 71 m due to a failure of a crown pillar, whereas, in reality, it was 60 × 30 × 80 m. The deformation results showed that the lamp room wherein the miners were trapped could have been displaced somewhere at or below level 5 but above level 6 towards the southwest direction. The output of surface disaster modeling was also satisfactory and reliable as the actual two slope failures, i.e. the western and southern landslides of the Lily gold mine, located in extreme risk zones as predicted by the model. The results of this study can be useful for future mine planning and the environmental improvements at the Lily gold mine. [ABSTRACT FROM AUTHOR]

Landslide Hazard Zonation (LSH) maps play a key role in landuse planning particularly in landslide prone areas. LSH mapping is globally accepted one for analyzing the area for landslide susceptibility. Different approaches were followed by many researchers in India to prepare landslide hazard zonation mapping depending upon their need and requirement. The Nilgiris district in Western Ghats of India is one of the severe to high landslide hazard prone areas of India. Many agencies have carried out research on LSH mapping for the Nilgiris district with different scales. A systematic study of inventory and zonation was 122carried out in 1980's by government agencies. However there is no proper updation or documentation on landslides after 1980's in the district The purpose of this paper is to review the existing landslide-related studies in the district of The Nilgiris and review the district's existing landslide hazard map with updated information. Landslide hazard maps in The Nilgiris were compiled in the GIS platform from various authenticated sources. Data on landslides from 1824 to 2014 were collected and a spatial database on landslides was created. A detailed inventory was analyzed and used for revision of the district's landslide hazard impact on the 2009 landslides.. Based on the landslide inventory and densely populated areas and repeated landslides at the same locations, the most landslide hazard areas were identified. [ABSTRACT FROM AUTHOR]

Although general approaches to the effect of water on the mechanisms causing landslides have been adopted, the work presented in this paper was carried out to quantify the landslide susceptibility variation in space and time, integrating the soil moisture distribution and routing (SMDR) model and landslide susceptibility concept. The approach proposed in the present study reflects the temporal effects of the saturation degree index (SDI) on landslide susceptibility as a new index to understand the effect of soil saturation. The topographic wetness index (TWI) is a conventional parameter that represents the relative wetness on landsliding. The new proposed landslide susceptibility approach is used in the study area to understand the effect of soil saturation and the emergence of the Derebaşı landslide in the study area. The comparative results of landslide susceptibility maps obtained from the new approach utilizing the proposed SDI and conventional TWI are remarkable. Accordingly, a new substantial method is proposed using the attainable monthly mean meteorological data to generate monthly landslide susceptibility maps. The results obtained for the Derebaşı landslide using the proposed method are validated with the other landslide that has occurred in the same watershed. The results revealed that the approach proposed in this study was compatible with the landslide mechanism in the study area and may help to express the water effect in landslide susceptibility analyses. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau is a landslide-prone area in Southwest China. A large-scale ancient landslide named Luanshibao Landslide on the eastern margin of the Tibetan Plateau is described in this paper, based on field investigation and satellite image interpretation. The geological setting and morphometric properties of the landslide are presented. Especially, the hummocky landscape, a prominent feature in the deposit area, is described. The total run-out distance of this landslide is 3910 m and the maximum velocity is estimated to be 36.6 m/s. The angle of reach is analysed as the index expressing the mobility of the landslide. Its value is about 12 degrees, indicating the high landslide mobility. Field observation shows that this landslide was most likely triggered by an earthquake, and the seismic acceleration capable to induce this landslide is estimated to be 0.36 g. This landslide might have changed the course of the Litang River. [ABSTRACT FROM AUTHOR]

Mountainous areas with steep slopes are vulnerable to landslide and often to avalanche according to the climate condition. In the rainy season, heavy rainfall causes landslides, and avalanche can be a serious threat to mountainous areas in winter. However, avalanche has not been emphasized relatively as compared with landslides in some nations like Korea. This paper estimates the landslide and avalanche hazard of the mountainous area with distinct seasons such as Provo Canyon in Utah and Seorak Mountain in Korea. To predict susceptibility of landslides and avalanches, several geomorphological factors were considered. These predictive factors were derived from digital elevation map, and the grid-based modeling was applied for landslide and avalanche susceptibility mapping within a geographical information system (GIS). To simulate debris flow and avalanche paths from the high potential areas, GIS-based surface flow analysis was used. As a result, this study provides information about ares prone to natural hazards, and it can be useful ancillary data for people attempting to avoid potentially hazardous areas. [ABSTRACT FROM AUTHOR]

The combination of remote sensing observations, geological field investigations and geomorphological mapping and measurements along with faults and landslides inventory were employed to identify the landslide-dams of Alborz Mountain belt in northern Iran. The study comprises main landslide-dams of Central Alborz, including those with saturated and dried lakes. Valasht, Chort, Shoormast, Evan, Tar, Havir and Imamza-e-Ali were the lakes discovered as being formed by a landslide-damming process. Several other ancient lakes were identified as the remnants of breached and dried landslide-dams. The geometry and structural setting of each case is described; however, more investigations are required to evaluate the potential hazards for downstream populations. Most of these cases are believed to be co-seismic or earthquake-induced landslide dams formed due to large (M: ~7.0) known historical and derived pre-historic earthquakes. This paper emphasizes the relationship between active faulting, fault valleys and slope instability that leads to fault valley blockage by landslide. The recommendation from this study is that more consideration of active structures is needed in tectonically active regions, where the fault damage zones are more susceptible to slope instability due to progressive deformation and earthquakes. [ABSTRACT FROM AUTHOR]

A landslide inventory map, which shows the location of landslide phenomena and contains information about movement type, activity, etc., is a basic element for landslide susceptibility and risk assessment. For this reason, the evaluation of the quality, in terms of accuracy and completeness, of landslide inventory maps is an important issue. In this paper, two landslide inventory maps are compared, in order to determine the corresponding quality, through a direct comparison, aimed to evaluate the degree of cartographic matching between the maps, and the determination of the statistical properties of landslide areas and the comparison between the frequency-area statistics of landslides contained in the two inventories. The two landslide inventory maps at 1:25,000 scale, used for these analyses, have been produced for Daunia region (Apulia, Southern Italy), by the Apulia River Basin Authority; the first, 'archive inventory', by unifying the existing archive inventories; the second, 'surveyed inventory', through aerial-photo interpretation and field investigations. [ABSTRACT FROM AUTHOR]

Water impoundment of the Three Gorges Reservoir has induced more than 5,000 landslides or potential landslides. These are huge threats to the safety of the dam and the people living in the reservoir area. The deformation characteristics and failure mode of the landslides are important factors in landslide stability evaluation and reinforcement design. The work reported in this paper used the Zhujiadian landslide as an example to study the deformation characteristics and failure mode of the landslides in the Three Gorges Reservoir region. Field investigations were conducted to analyze the structure of the landslide. Then the deformations of the landslide were observed using an integrated monitoring system, including extensometer monitoring for crack development, GPS monitoring for surface displacement, and inclinometer monitoring for internal displacement. The results obtained suggest that the Zhujiadian landslide is a multi-rotational landslide containing three main rotational sub-landslides at present. The deformation and failure of this landslide have developed from front to back. The failure of the front part opens up space for the back part to slide forward into. The coupled actions of the reservoir water and rainfall have led to the failure of the Zhujiadian landslide. Finally, a risk evaluation of the water surge potentially generated by the landslide was conducted by calculating the maximum wave height at the opposite side of the river. The findings of this paper provide some useful information for the stability evaluation and reinforcement design of landslides in similar geological settings in the Three Gorges Reservoir area and in other parts of the world. [ABSTRACT FROM AUTHOR]

Introduction: The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method: This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. Conclusion: The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas. [ABSTRACT FROM AUTHOR]

Soil is a fundamental resource with its value vital to the world's ecosystem. Due to the fact that soil is one of the bases of all terrestrial life, humans cannot survive without it. However, soil erosion has jeopardized soil sustainability and affected the environmental quality, leaving a bad impact if these issues were not tackled at an earlier phase. Many research has been done to predict soil erosion susceptibility areas using different methods. This research aims to classify the contributing factors of soil erosion according to the risk and generate a soil erosion risk prediction map in Cameron Highlands. Thus, this research focuses on a knowledge-driven method that uses Analytical Hierarchy Process (AHP) technique to achieve the objectives. This technique consists of weighing the factors adopted by comparing pairs of factors that control erosion in this area through experts' opinions. 15 factors have been chosen to build the prediction map. Result shows that rainfall erosivity is the main factor contributing to soil erosion in Cameron Highlands which is 0.110, followed by land use (0.095), slope steepness (0.089), soil texture (0.079), NDVI (0.079), TWI (0.072), slope length (0.065), slope aspect (0.064), slope altitude (0.062), SPI (0.061), lithology (0.060), slope curvature (0.054), drainage density (0.049), distance to road (0.029) and distance to stream (0.025). The west part of the study area was exposed to a high risk of soil erosion. This research will give the decision-makers, policymakers and planners insight into minimizing the soil erosion problem and suggest better precautions and solutions to overcome this severe environmental problem in the more advanced phase. [ABSTRACT FROM AUTHOR]

Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages. [ABSTRACT FROM AUTHOR]

Abstract  Based on the fault-block concept, the paper proposes a scheme for the seismic geo-hazard assessment of engineering sites. A general geo-hazard intensity is determined by comparing the basic intensity of the block where the engineering structure is to be located and the influencing intensity associated with the potential epicenter of a strong earthquake in the boundary fault zone. The obtained general intensity is then modified to take into account the local ground conditions and pre-existing geo-hazard level (particularly landslides) to obtain the comprehensive intensity, which is the basis for the seismic geo-hazard level assessment and site planning recommendations. Four dam sites associated with major hydropower stations are discussed, as well as the 100 m high Zipingpo dam which is located close to the epicenter of the Wenchuan earthquake. The results show that a dam designed on an intensity close to that proposed in the paper would be able to resist potential destructive earthquakes without significant distress. [ABSTRACT FROM AUTHOR]

Accurate and reliable displacement prediction is vital for developing landslide early warning system, since displacement variations directly reveal the evolution of landslide. The past decade has seen the rapid development of displacement prediction technology. Most of the existing studies are based on deterministic point prediction models, with the goal of obtaining high-precision predictions. However, these pointwise predictions do not give any indication of credibility, which makes it difficult to make accurate decisions for subsequent landslide treatment. To address this problem, a prediction interval estimation method, instead of point prediction, for landslide displacement is proposed. In the methodology, double exponential smoothing is employed to deal with the nonlinear cumulative displacement, while two support vector machines are proposed to directly generate the displacement lower boundary and upper boundary. To obtain optimal model parameters, the adaptive chicken swarm optimization using chaotic mapping and adaptive inertia weight strategies is proposed to minimize a modified prediction interval-based objective function. A real-world landslide displacement data set is used to demonstrate the proposed method. Experimental results show that it is a promising tool for the construction of high-quality prediction intervals, which can inform the landslide treatment-related decision-making. [ABSTRACT FROM AUTHOR]

Understanding farmer's agricultural production decisions regarding natural hazards plays crucial role for extension of organizations and policymakers to facilitate farmers to make optimal adaptive strategies. The aspect of determinants affecting the farmer's preference of measure in adaptation procedure investigated in limited studies of literature. This study focused this research gap in scenario of Pakistan and investigated rural households' decision-making adaptation strategies process regarding landslides and flash floods hazards. The research work used household survey data of 398 purposively selected farmers of district Shangla from Khyber Pakhtunkhwa Province of Pakistan. This study used manifold-portfolio structure, assumes as farmers have several preferences of adaptation strategies all together explained adaptation options are symbiotic. Multivariate probit models were employed in this paper for the empirical estimation of the study. Variegation of crops, crop-type diversifications, cropping pattern changing, crop protection and implementation management are primary adaptation measures used by respondent households. Inadequate information regarding landslides and flash floods, lack of technical equipments and machinery, insufficient money and limited support from local government were considered foremost limits in this study region. The study conclusion indicated as farmers' perceptions, farming descriptions, farmers' socioeconomic characteristics, and institutional conditions strongly affects farmers' adaptation assessment about landslides and flash floods. In future policies, it is more appropriate as major involvement of these causative factors to make possible appropriate adopting adaptation strategies for inhabitants of local areas. [ABSTRACT FROM AUTHOR]

The disaster caused by landslide is huge. To prevent the spread of the disaster to the maximum extent, it is particularly important to carry out landslide disaster treatment work. The selection of landslide disaster treatment alternative is a large scale group decision-making (LSGDM) problem. Because of the wide application of social media, a large number of experts and the public can participate in decision-making process, which is conducive to improving the efficiency and correctness of decision-making. A IF-TW-LSGDM method based on three-way decision (TWD) and intuitionistic fuzzy set (IFS) is proposed and applied to the selection of landslide treatment alternatives. First of all, considering that experts and the public participate in the evaluation of LSGDM events, respectively, the method of obtaining and handling the public evaluation information is given, and the information fusion approach of the public and experts evaluation information is given. Second, evaluation values represented by fuzzy numbers are converted into intuitionistic fuzzy numbers (IFNs), and the intuitionistic fuzzy evaluation decision matrix described by IFNs is obtained. Then, a new LSGDM method of alternatives classification and ranking based on IFS and TWD is proposed, the calculation steps and algorithm description are given. In this process, we first cluster the experts, then consider the identification and management of non-cooperative behavior of expert groups. This work provides an effective method based on LSGDM for the selection of landslide treatment alternatives. Finally, the sensitivity of parameters is analyzed, and the feasibility and effectiveness of this method are compared and verified. [ABSTRACT FROM AUTHOR]

The aim of this study was to map the landslide risk at the Alamut watershed based on relative vulnerability and susceptibility. The potential of damage to the resources was considered landslide vulnerability. The fuzzy gamma operators were also used to assess landslide susceptibility. Thematic layers of 10 causal factors including slope, aspect, altitude, land use, lithology, distance to road, distance to stream, distance to fault, peak ground acceleration and mean annual precipitation were prepared. The landslide inventory map comprising 40 landslides covering 1417 hectares was partitioned into two subsets including 70% for training and 30% for testing. The Dr and Qs indices were applied to compare the validity of the landslide susceptibility maps. The spatial landslide risk was obtained by multiplying the landslide susceptibility and landslide vulnerability. The results show that the LSM derived by gamma of 0.95 has the most validity with Qs equal to 1.93. The ascending trend of the Dr index for low to high classes implies the correct classification of the LSM. The most important role in the occurrence of landslides has been related to lithology and land use factors. Although residential areas cover a small area of the watershed, 84.35% of the very high-risk class and 91.21% of the high-risk class are located in these areas. These results imply that in the Alamut watershed, the principles of land use planning have not been considered for landslide management. Therefore, the results of this study can be very useful for landslide risk management in the region. [ABSTRACT FROM AUTHOR]

Background: Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu's algorithm). Objectives: The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Summary: The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies. [ABSTRACT FROM AUTHOR]

This study aims to explore the effects of different non-landslide sampling strategies on machine learning models in landslide susceptibility mapping. Non-landslide samples are inherently uncertain, and the selection of non-landslide samples may suffer from issues such as noisy or insufficient regional representations, which can affect the accuracy of the results. In this study, a positive-unlabeled (PU) bagging semi-supervised learning method was introduced for non-landslide sample selection. In addition, buffer control sampling (BCS) and K-means (KM) clustering were applied for comparative analysis. Based on landslide data from Qiaojia County, Yunnan Province, China, collected in 2014, three machine learning models, namely, random forest, support vector machine, and CatBoost, were used for landslide susceptibility mapping. The results show that the quality of samples selected using different non-landslide sampling strategies varies significantly. Overall, the quality of non-landslide samples selected using the PU bagging method is superior, and this method performs best when combined with CatBoost for predicting (AUC = 0.897) landslides in very high and high susceptibility zones (82.14%). Additionally, the KM results indicated overfitting, displaying high accuracy for validation but poor statistical outcomes for zoning. The BCS results were the worst. [ABSTRACT FROM AUTHOR]

Landslides cause significant damage to property and life in the West Jaintia Hills district in Meghalaya, India, due to mountains, slopes, and extreme rainfall. Landslide susceptibility maps are highly efficient and helpful for disaster preparation and management. The determination of this work is to create a landslide susceptibility map (LSM) for West Jaintia Hills using the analytical hierarchy process (AHP) method. The previous landslide data are required for effective management of the current situation, and these data are divided into training samples (75%) and testing samples (25%) for predicting efficiency. The different landslide causative factors are investigated in this study: rainfall, slope, geomorphology, elevation, lineament density, LULC, distance from a road, NDWI, MSAVI, and NDVI. The causal factors are subdivided and weighted according to AHP, and a susceptibility map is prepared. According to the developed map, 19% of the region's total area is low susceptible, 52% moderate, 25% high, and 4% very highly susceptible. Field data and the receiver operating characteristics (ROC) approach are used to validate the produced map. The outcomes indicate that the predicted susceptibility map closely reflects previous and recent landslide events. The created map is accurate and can be applied to future land-use planning. [ABSTRACT FROM AUTHOR]

Seismic landslides are dangerous natural hazards that can cause immense damage to human lives and property. Susceptibility assessment of earthquake-triggered landslides provides the scientific basis and theoretical foundation for disaster emergency management in engineering projects. However, landslide susceptibility assessment requires a massive amount of historical landslide data. Evidence of past landslide activities may be lost due to changes in geographical conditions and human factors over time. The lack of landslide data poses difficulties in assessing landslide susceptibility. The aim of this study is to establish a generalized seismic landslide susceptibility assessment model for applying it to the Dayong highway in the Chenghai area, where earthquakes occur frequently but with a lack of landslide data. The landslide data used comes from the 2014 Ludian Ms (Surface wave magnitude) 6.5 earthquake in a region with geographical conditions similar to those in the Chenghai area. The influencing factors considered include elevation, slope, slope aspect, distance to streams, distance to faults, geology, terrain wetness index, normalized difference vegetation index, epicenter distance and peak ground acceleration. The frequency ratio method is used to eliminate influencing factors with poor statistical dispersion of landslides. Principal component analysis (PCA) is utilized to reduce the dimensionality of landslide conditioning factors and to improve the transferability of the assessment model to different regions. A support vector machine model is used to establish the susceptibility assessment model. The results show that the accuracy of the PCA–SVM model reaches 93.6%. The landslide susceptibility of the Chenghai area is classified into 5 classes, with the "Very high" landslide susceptibility class accounting for 0.63%. The 13-km section in the middle of the Dayong highway, which accounts for 8.9%, is identified as the high-risk area most obviously impacted by seismic landslides. This study provides a new approach for seismic landslide susceptibility assessment in areas lacking in landslide inventory data. [ABSTRACT FROM AUTHOR]

In the reference list of the published online paper, the information captured below is wrong: Ramesh, M. V. (2017). Slope stability investigation of Chandmari in Sikkim, Northeastern India. In Workshop 390 on World Landslide Forum (pp. 363-369). Springer, Cham. [ABSTRACT FROM AUTHOR]