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Context: The landslides in Tijuana, Mexico, destroy a large number of structures and infrastructures each year, producing large losses in various sectors of the economy. Method: In this study, we carry out a quantitative assessment of the landslides hazard in a neighborhood of the city that was affected by a landslide in 2010 and shows signs of terrain instability that warn about the possibility of new landslides. The hazard was calculated using the spatial probability, based on the susceptibility of the terrain to landslides, and the temporal probability using a database of events that occurred at sites near the study area. We apply deterministic methods based on the analysis of slope stability to calculate susceptibility, and we estimate the temporal probability using probability models that consider the occurrence of independent random events. Results: it was obtained that more than 50% of the study area presents a high landslides hazard of for return periods of 5, 10, 15 and 20 years, and it is demonstrated that the seismicity, topography and geotechnical properties of the soils, they are the factors with the greatest influence on terrain instability. In addition, it was determined that the areas of potential landslides are in soils whose resistance has been reduced due to the presence of underground flows produced by the infiltration of water through existing cracks and fractures in the terrain. Conclusions: the application of the described procedure made it possible to quantify the landslides hazard in the Laderas de Monterrey neighborhood for four return periods and to identify the factors with the greatest influence on the occurrence of these phenomena. The results obtained are an important step to analyze and evaluate the risk that landslides represent for structures, infrastructures, and people exposed to the impact of these phenomena; and they are a valuable tool for decision-making related to risk management and the establishment of regulations on land use in the area.

Predicting rainfall-induced landslide motion is challenging because shallow groundwater flow is extremely sensitive to the preexisting moisture content in the ground. Here, we use groundwater hydrology theory and numerical modeling combined with five years of field monitoring to illustrate how unsaturated groundwater flow processes modulate the seasonal pore water pressure rise and therefore the onset of motion for slow-moving landslides. The onset of landslide motion at Oak Ridge earthflow in California’s Diablo Range occurs after an abrupt water table rise to near the landslide surface 52–129 days after seasonal rainfall commences. Model results and theory suggest that this abrupt rise occurs from the advection of a nearly saturated wetting front, which marks the leading edge of the integrated downward flux of seasonal rainfall, to the water table. Prior to this abrupt rise, we observe little measured pore water pressure response within the landslide due to rainfall. However, once the wetting front reaches the water table, we observe nearly instantaneous pore water pressure transmission within the landslide body that is accompanied by landslide acceleration. We cast the timescale to reach a critical pore water pressure threshold using a simple mass balance model that considers variable moisture storage with depth and explains the onset of seasonal landslide motion with a rainfall intensity-duration threshold. Our model shows that the seasonal response time of slow-moving landslides is controlled by the dry season vadose zone depth rather than the total landslide thickness.

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk. [ABSTRACT FROM AUTHOR]

The purpose of this study is to map and geographically locate areas with a high probability of natural events occurring such as "rockfall". In this context, a multi-method approach is proposed to characterize and evaluate the potential vulnerability of an unstable ground as well as rockfall risk, through morpho-tectonic, geological, and morphometric analysis integrated into a geographic information system (GIS). Several parameters such as slope, lithology, lineaments, morphology, land use, and rainfall have been integrated and indexed in a geographic information system to be able to develop the rockfall risk map that allowed us to detect and geographically delimit the unstable areas in the Korbous cliff. The studied area presents a contrasted territory, from both morphological and geological points of view. The steep slopes' rockfalls are quite frequent and constitute a potential risk to infrastructure and habitats at the bottom of slopes such as Korbous village, Ain Oktor, and MC 128 road. The proposed rockfall risk map, in this study, would serve as a guide and decision-support tool for the choice of preventive measures for reducing disaster risk (RDR). [ABSTRACT FROM AUTHOR]

Landslide hazard assessment is crucial for landslide monitoring and early warning. A novel method based on deep learning model for national-scale landslide hazard assessment was proposed in this study, which contains three stages: (1) landslide susceptibility analysis using three hybrid neural networks of convolutional neural network-simple recurrent unit (CNN-SRU), convolutional neural network-long short-term memory (CNN-LSTM), and convolutional neural network-gated recurrent unit (CNN-GRU); (2) landslide temporal probability prediction using the proposed spatiotemporal transformer (ST-transformer) to address the time uncertainty of rainfall threshold calculation in landslide temporal probability prediction and the matter without considering the geographical regional differences of landslide spatiotemporal probability weights; and (3) quantitative landslide hazard calculation with the preceding results using the improved landslide hazard formula. The validation of this method was conducted in conterminous United States, where the results of steps (1) and (2) demonstrated excellent performance compared with existing works. Consequently, the calculation derived from the previous two steps was effectively used for landslide hazard assessment using the improved landslide hazard formula, and its reliability was confirmed through the validation of actual landslide events. The proposed method is of practical significance for national-scale landslide hazard assessment. [ABSTRACT FROM AUTHOR]

Landslides are one of the most common geological hazards, with rainfall being the main trigger for their occurrence. In order to find the impact of rainfall conditions on slope stability and determine the rainfall threshold curve, the saturated-unsaturated seepage and stability analysis of three small slopes in Anhui province was carried out using FLAC2D 7.0 numerical simulation software, and the changing trend of safety factor of slopes with rainfall intensity and duration was obtained. The results show that the influence of rainfall on slopes is limited; the rainfall intensity corresponding to the minimum safety factor of slopes during the rainfall infiltration process is the ultimate rainfall intensity, and the longer the rainfall duration, the smaller the ultimate rainfall intensity becomes and tends to stabilize; the changes in pore pressure and saturation at the monitoring location on the slope further verify that the slope tends to become saturated under ultimate rainfall intensity, resulting in the minimum safety factor. Subsequent rainfall has little effect on the safety factor of slope. To be on the safe side, a critical safety factor of 1.15 was assumed, and the corresponding rainfall intensity was defined as the critical rainfall intensity. Similar to the trend of ultimate rainfall intensity, the longer the rainfall duration, the smaller the critical rainfall intensity becomes and tends to stabilize. The critical rainfall intensities for the three small slopes tend to stabilize at 13, 12, and 16 mm?d-1, respectively, with corresponding rainfall durations of 5, 9, and 12 d. It is indicated that under the influence of long-term early-stage rainfall, a small amount of rainfall can cause slope instability and failure. Additionally, the critical rainfall intensity data of these three small slopes(simulated data points for geological disaster rainfall)were used to fit the rainfall threshold curve, and the accuracy of the rainfall threshold curve was validated with 435 sets of historical rainfall data for geological disasters in Anhui province from 2008 to 2022. The verification results show that over 95% of actual disaster rainfall data can be predicted by the rainfall threshold curve. [ABSTRACT FROM AUTHOR]

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The main objective of this work is to evaluate the landslide hazard using temporal and spatial probability analysis at a large scale for the urban area of Azazga in northern Algeria. The historical landslide events for a period from 1952 to 2019 were collected and mapped from various sources, and a database was built. The daily and cumulative antecedent rainfall data related to landslide events were prepared from rain stations located in and around the urban area of Azazga. The rainfall threshold model for landslide occurrence was developed by analyzing the relationship between the daily and cumulative values of antecedent rainfall related to landslide events. The results show that 30-day antecedent rainfall is the most reliable predictor of current landslide events. The resultant rainfall threshold model was validated further using the 2012 rainfall and landslide events, which are not included in the threshold model. The results were used to estimate the temporal probability of a landslide occurrence using a Poisson probability model. For the spatial prediction probability of landslide initiation, four landslide susceptibility maps were previously prepared using statistical models. Finally, landslide hazard maps were produced by combining the temporal and spatial probabilities of landslides for three return periods: 1, 3, and 5 years. The obtained results could be considered as a first step toward reducing landslide risk and developing an early warning system. [ABSTRACT FROM AUTHOR]

To accurately quantify landslide hazard in a region of Turkey, we develop new marked point-process models within a Bayesian hierarchical framework for the joint prediction of landslide counts and sizes. We leverage mark distributions justified by extreme-value theory, and specifically propose 'sub-asymptotic' distributions to flexibly model landslide sizes from low to high quantiles. The use of intrinsic conditional autoregressive priors, and a customised adaptive Markov chain Monte Carlo algorithm, allow for fast fully Bayesian inference. We show that sub-asymptotic mark distributions provide improved predictions of large landslide sizes, and use our model for risk assessment and hazard mapping. [ABSTRACT FROM AUTHOR]

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models' remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv-LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS). [ABSTRACT FROM AUTHOR]

The definition of landslide hazard is a step-like procedure that encompasses the quantification of its spatial and temporal attributes, i.e., a reliable definition of landslide susceptibility and a detailed analysis of landslide recurrence. However, available information is often incomplete, fragmented and unsuitable for reliable quantitative analysis. Nevertheless, landslide hazard evaluation has a key role in the implementation of risk mitigation policies and an effort should be done to retrieve information and make it useful for this purpose. In this research, we go through this topic of optimising the information available in catalogues, starting from landslide inventory review and constitution of a boosted training dataset, propaedeutic for susceptibility analysis based on machine learning methods. The temporal recurrence of landslide events has been approached here either through the definitions of large-scale quantitative hazard descriptors or by analysis of historical rainfall (i.e., the main triggering factor for the considered shallow earth slope failures) databases through the definition of rainfall probability curves. Spatial and temporal attributes were integrated, selecting potential landslide source areas ranked in terms of hazard. Data integration was also pursued through persistent scatterer interferometry analysis which pointed out areas of interest within potential landslide source areas featured by ongoing ground movement. The consequential approach led to the definition of the first hazard product of the city of Rome at a local scale functional for advisory purposes or the statutory level, representing a thematic layer able to orient the risk managers and infrastructure stakeholders. [ABSTRACT FROM AUTHOR]

Problem statement. Azerbaijan is making a lot of efforts to reduce the impact of dangerous geological processes on natural geosystems, but they still cause huge damage. To a greater extent, the region of the Greater Caucasus, namely the southern slope, is subject to such processes, where the whole range of dangerous geological processes occurs: earthquake (7-8 b and above), landslides, landslides, screes, mudflows, etc. All of them are large-scale processes in terms of damage - they affect large areas and lead to economic losses. Purpose - to identify the main factors of the formation and spread of landslides in the basin of one of the most mudflow-bearing rivers not only in Azerbaijan, but also in the South Caucasus - the Agsuchay river, identify the conditions for their formation, assess the risk of the territory's susceptibility to landslide processes, as well as ways to prevent and protect. Research method. To assess landslide susceptibility and create maps of the potential development of landslides in the basin of the Agsuchay river we used the Frequency Ratio method (FR). Research results. For minimize damage from landslides on the example of the Agsuchay river basin a detailed study of the factors (hypsometry, slope angles (slope steepness) was carried out by us. Also slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion network, distance to roads and land use) that determine the development of landslide processes with taking into account the mechanism of their development, as well as an analysis of the obtained values of landslide susceptibility and their potential development was studied. In the ArcGIS software environment, using the “Raster Calculator” spatial analysis tool, summing up each landslide factor multiplied by its weights, a map of the landslide susceptibility of the Agsuchay river basin was obtained. In the river basin Agsuchay we identified over 120 landslide areas. Most of the landslides were recorded along the Baskal tectonic cover, the Steppe Plateau, as well as on the slopes of the Langyabiz ridge, and also partially on the slopes of the Nialdag ridge. Conclusion. Using the natural boundary classification method in the ArcGIS software environment, the study area was divided into five landslide potential zones: very low, low, medium, high, and very high. The result of the analysis showed that zones with very low, low, medium, high and very high landslide development potential are: 13.75; 24.48; 31.51; 20.51 and 9.74% of the study area, respectively. Ultimately, the reliability of the obtained models was evaluated using AUC ROC (area under the error curve) analysis, which showed high performance of the method used (82%). Due to the high reliability, the method used can be used to assess the landslide susceptibility not only of the territory of Azerbaijan, but of similar regions of the Alpine-Himalayan belt.

Kendari is the capital of Southeast Sulawesi Province which is prone to landslides. Good land management needs to be done to minimize the impact of landslides. This study aimed to map the Kendari landslide hazard that can be used as an input into land management strategy, especially in vulnerable to the threat of landslides. The primary data used in this study were DEMNAS and Sentinel-2. Landslide detection was carried out using a Process Hierarchy Analysis (AHP) approach and validated by field surveys. Land capability analysis was based on landform analysis using land system data. Land management directions were carried out based on the integration of landslide hazard analysis with the ability of the land to be calibrated with actual land cover. The analysis showed that areas with high and very high landslide hazards reached 2654.09 ha (9.64%) and 4354.78 ha (15.82%). Capability class of VII is spread over structural hills to the north and south of Kendari with an area of 7,215.81 ha (26.21%). Land management in areas with very high landslide hazards and land capability class VII is to add cover crops on land that is not protected by a canopy. Cover crops that can be added are the grass type to minimize the danger of erosion that can trigger landslides.

AbstractThe improvement of the landslide susceptibility mapping (LSM) is a long-standing problem, as it provides basics for hazard mitigation. Recently, hybrid ensemble deep learning (DL) techniques have witnessed the potential for this purpose. In this paper, we proposed a novel ensemble DL model, namely GL-ResNet, which employs the conventional ResNet blocks for landslide feature extraction, long-short term memory (LSTM) structures for information storage, and a proposed GoogLeNet block (GBlk) to broaden model perception ability. To validate the model performance, a landslide inventory containing 1147 historical landslide polygons and the data of 12 landslide factors in the Wenchuan area in southwestern China, was presented and separated into training and validating dataset using a 7:3 randomly sampling ratio strategy. Based on AUC and Accuracy, GL-ResNet (0.96 and 0.909) outperformed logistic regression (0.92 and 0.851), support vector machines (0.94 and 0.884), deep belief networks (0.95 and 0.884), gated recurrent unit (0.94 and 0.884) and ResNet (0.95 and 0.894). We also explored the robustness of GL-ResNet for LSM. The results suggested that although GL-ResNet is sensitive to initial training conditions, it showed good robustness to model training method and sample ratios. In detail, GL-ResNet outperformed the conventional models in terms of fitting power and prediction performance by 0.03-0.04 and 0.02 respectively in most cases, with even greater differences in the limited training dataset.

The assessment of earthquake-induced landslide hazards is an important prerequisite for disaster prevention and reduction in tectonic active areas. However, few studies have considered the amplification effect of site and topography on ground motion parameters and made the peak ground acceleration (PGA) correction. Based on Newmark's method, taking Litang County, Sichuan Province, China, as the study area, considering the site amplification effect and topographic amplification effect, this paper carried out the assessment of landslide hazards under the action of occasional earthquakes with an exceedance probability of 10% and rare earthquakes with an exceedance probability of 2%. The results show that the hazard of earthquake-induced landslides is higher in the high slopes of loose rock in the middle of Litang County and the steep rock slopes with large topographic relief in the northeast, and low in the southern plateau and central basins. The site and topographic conditions have a significant effect on the nonlinear amplification of PGA, and the corrected PGA is even magnified by 2–3 times in steep mountains. Compared with the occasional earthquakes, the influence of rare earthquakes on the initiation and movement distance of landslides is remarkably improved. This study can provide a valuable reference for potential earthquake-induced landslide hazard assessment and seismic landslide emergency response in Litang County. [ABSTRACT FROM AUTHOR]

Landslides are caused by rainfall as one of the main factors. In order to study the effect of rainfall on the physical and mechanical parameters of landslides, a physical model of the colluvium landslide is created in laboratory conditions with silty clay, river sand, and gravel, taking Shuping landslide in the Three Gorges Reservoir area as the prototype. The artificial rainfall is applied to the accumulation model, which is steady for 60 h, and then the gravel soil is taken out along the different elevations of the colluvium for the permeability test and direct shear test, and the evolution law of changes in porosity, the permeability coefficient, and the shear strength parameters along the elevation are studied. Combined with XRF and NMR tests, the spatial variation of the permeability coefficient and shear strength parameters is discussed from the perspective of chemical elements, minerals content, and porosity, and the stability analysis of a colluvium landslide is carried out considering the influence of parameters along the elevation. The results show that under the action of rainfall seepage, the fine particles of clay are transported from upslope to downslope, resulting in more and more fine particles of clay at the toe slope. The original pores are gradually filled, the cementation between particles is stronger, the corresponding cohesion is increased, and the permeability coefficient is reduced. Due to the loss of fine particles at the upslope, the relative content of coarse particles increases, leading to an increase in the internal friction angle. The variability of the slope's physical and mechanical parameters is a result of the spatial transport of clay particles in the colluvium caused by the rainfall seepage above. Specifically, the permeability coefficient and internal friction angle from upslope to downslope decrease linearly under the action of rainfall, but the law of the cohesion increases linearly. The upslope's permeability coefficient and internal friction angle decrease by 11% and 8% compared to those of the downslope, while the cohesion increases by 168%. The results of FLAC3D numerical calculation of Shuping landslide show that the maximum deformation in the X direction of 145 m and 175 m water level increases by 12% and 42%, and the safety factor decreases by 0.63% and 5% under the combined action of rainfall and the reservoir water level, that is, when considering the variation of parameters along the elevation of the landslide. The research findings provide a better understanding of the spatial parameters in similar colluvium bodies under rainfall action. [ABSTRACT FROM AUTHOR]

Objectives: In July 2021, Henan Province of China suffered continuous extreme rainfalls, causing widespread flood and serious paralysis of the highway network. The flood caused by the heavy rainfalls brought enormous loss to people􀆳s lives and properties in Henan Province. Methods: We collectively used a range of earth observations, namely remote sensing images from Sentinel-1 and Gaofen-3 satellites, rainfall and water vapor data from the European Centre for Medium-Range Weather Forecasts, and geohazard survey data to investigate and analyze the evolving process of the flood caused by the heavy rainfalls in Henan Province, and proposed a new technical framework for rapid assessment of traffic inefficiency under flood scenarios over wide regions. Results: The application of the new framework to the case in Henan Province suggests that: (1) The cumulative rainfall was highly concentrated, reaching a historic high level and affecting a wide region. (2) The total affected area in Zhengzhou City, Henan Province and its surrounding areas reached 3 800 km². (3) The potential of secondary landslides in mountainous areas such as Sanmenxia City and Dengfeng City was highly increased.(4) About 1 300.46 km of major roads were affected by this flood, and the overall connectivity of major highway networks in Zhengzhou and its surrounding five cities decreased about 21.27%, with 34.22% for expressways, 13.78% for national highways, and 14.86% for provincial highways. Conclusions: This study shows how to integrate remote sensing, secondary landslide analysis, road network analysis, and other multi‐field technologies to construct a framework for traffic inefficiency assessment under natural hazards. It is believed that this framework could be applied to other hazardous events and provide valuable information for emergency rescue. [ABSTRACT FROM AUTHOR]

For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the early-warning-system or to the rainfall-threshold themes. In this context, few published research have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size, has hardly ever been modeled over space and time. However, the technological advancements of data-driven models have reached a level of maturity that allows to model all three components (Where, When and Size) mentioned above. This work, takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this ambitious task, we have used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake on the 25th of April 2015. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 x 1 km and classified/regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6-month resolution depending on the mapped inventory dates. The results are promising as our model performs satisfactorily both in the classification (susceptibility) and regression (density prediction) tasks. We believe that the model we propose brings a level of novelty that has the potential to create a rift with respect to the common susceptibility literature, finally proposing an integrated framework for hazard modeling in a data-driven context. To promote reproducibility and repeatability of the analyses in this work, we share data and codes in a github repository accessible from this link. [ABSTRACT FROM AUTHOR]

Kendari is the capital of Southeast Sulawesi Province which is prone to landslides. Good land management needs to be done to minimize the impact of landslides. This study aimed to map the Kendari landslide hazard that can be used as an input into land management strategy, especially in vulnerable to the threat of landslides. The primary data used in this study were DEMNAS and Sentinel-2. Landslide detection was carried out using a Process Hierarchy Analysis (AHP) approach and validated by field surveys. Land capability analysis was based on landform analysis using land system data. Land management directions were carried out based on the integration of landslide hazard analysis with the ability of the land to be calibrated with actual land cover. The analysis showed that areas with high and very high landslide hazards reached 2654.09 ha (9.64%) and 4354.78 ha (15.82%). Capability class of VII is spread over structural hills to the north and south of Kendari with an area of 7,215.81 ha (26.21%). Land management in areas with very high landslide hazards and land capability class VII is to add cover crops on land that is not protected by a canopy. Cover crops that can be added are the grass type to minimize the danger of erosion that can trigger landslides. [ABSTRACT FROM AUTHOR]

Land take and soil sealing imply land cover transitions that may possibly result in decreased capacity to resist landslides; hence, this study focuses on the relations between land-taking processes and landslide hazard by addressing the following research question: "To what extent do land-taking processes increase landslide hazard?" The impact of land take is assessed through a regression model which relates the level of landslide hazard to a set of land cover variables which include artificialized land; that is, land taken up through urbanization processes, and a set of covariates that represent land cover types grouped in accordance with the LEAC (land and ecosystem accounting) classification. This methodological approach is implemented into the spatial context of Sardinia, an insular Italian region, and shows that not only the amount of taken up artificialized land, but also other types of land covers, are likely to increase the magnitude of landslide hazard. A set of implications concerning planning policies related to land cover and land cover transitions are discussed in the concluding section, where policy recommendations are identified in order to mitigate the impacts of land cover transitions on landslide hazards. [ABSTRACT FROM AUTHOR]

Problem statement. Azerbaijan is making a lot of efforts to reduce the impact of dangerous geological processes on natural geosystems, but they still cause huge damage. To a greater extent, the region of the Greater Caucasus, namely the southern slope, is subject to such processes, where the whole range of dangerous geological processes occurs: earthquake (7-8 b and above), landslides, landslides, screes, mudflows, etc. All of them are large-scale processes in terms of damage - they affect large areas and lead to economic losses. Purpose - to identify the main factors of the formation and spread of landslides in the basin of one of the most mudflow-bearing rivers not only in Azerbaijan, but also in the South Caucasus - the Agsuchay river, identify the conditions for their formation, assess the risk of the territory's susceptibility to landslide processes, as well as ways to prevent and protect. Research method. To assess landslide susceptibility and create maps of the potential development of landslides in the basin of the Agsuchay river we used the Frequency Ratio method (FR). Research results. For minimize damage from landslides on the example of the Agsuchay river basin a detailed study of the factors (hypsometry, slope angles (slope steepness) was carried out by us. Also slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion network, distance to roads and land use) that determine the development of landslide processes with taking into account the mechanism of their development, as well as an analysis of the obtained values of landslide susceptibility and their potential development was studied. In the ArcGIS software environment, using the “Raster Calculator” spatial analysis tool, summing up each landslide factor multiplied by its weights, a map of the landslide susceptibility of the Agsuchay river basin was obtained. In the river basin Agsuchay we identified over 120 landslide areas. Most of the landslides were recorded along the Baskal tectonic cover, the Steppe Plateau, as well as on the slopes of the Langyabiz ridge, and also partially on the slopes of the Nialdag ridge. Conclusion. Using the natural boundary classification method in the ArcGIS software environment, the study area was divided into five landslide potential zones: very low, low, medium, high, and very high. The result of the analysis showed that zones with very low, low, medium, high and very high landslide development potential are: 13.75; 24.48; 31.51; 20.51 and 9.74% of the study area, respectively. Ultimately, the reliability of the obtained models was evaluated using AUC ROC (area under the error curve) analysis, which showed high performance of the method used (82%). Due to the high reliability, the method used can be used to assess the landslide susceptibility not only of the territory of Azerbaijan, but of similar regions of the Alpine-Himalayan belt. [ABSTRACT FROM AUTHOR]

The Western Himalayan Syntaxis area is located near the Pamir Plateau. The geological structure is active and geological disasters occur frequently in this area. In this study, we employed the Google Earth platform and visual interpretation to identify ancient landslides in the region and to establish a regional ancient landslide database. Then, nine landslide-influencing factors (elevation, slope, aspect, curvature, distance to the river, distance to a glacier, lithology, distance to fault and distance to the epicenter of earthquakes above magnitude 5) were examined using ArcGIS software. The spatial distribution of landslides were analyzed statistically. Finally, an IV model and WoE model were used to evaluate the regional landslide hazard and the evaluation results were verified via a confusion matrix and a receiver operating characteristic (ROC) curve. The landslide database contained 7,947 landslides in this area with a total area of 3747.27 km2. Landslides were mostly developed at an elevation of 4,000–5,000 m, a slope of 15–25°, a north aspect, curvature of −0.33 to 0.33, distance to the water system of 1,000–2000 m, distance to a glacier of 2000–5,000 m, Permian sandstone, siltstone, argillaceous sandstone and Triassic siltstone, conglomerate and fine conglomerate, and distance to a fault of 20,000–50,000 m. The accuracy of the IV and WoE models was relatively high. The comprehensive accuracy of the confusion matrix of the two models was above 70% and the AUC value of the ROC curve was above 75%. The landslide database of the Western Himalayan Syntaxis was established and the landslide distribution and hazard assessment results can be used as a reference for landslide disaster prevention and mitigation and engineering construction planning in this area.

This study reported a landslide hazard pattern considering the dependence of hazard components, with the example of Heilongjiang Province. The spatial and magnitude probabilities of landslides were first constructed to develop a potential link with the annual occurrence rate. Thereafter, landslide hazard considering component dependence was derived based on a modified Poisson model, and presented via the exceeding probability under the scenarios of combining two magnitudes (greater than or equal to 30,000 and 100,000 m³ ) and four periods (1, 3, 5 and 10 years). Results show that the Poisson model refined is not only a temporal probability function conditional on the magnitude and space, but can capture the hazard definition accurately. Moreover, the exceeding probability is related to the location of the raster, magnitude and the time interval of the landslide. Geographically, the areas with high exceeding probability are concentrated in the central and southeastern parts of the study area. Further, landslide probability dynamically varies with the magnitude and time interval, increasing gradually with the time interval, but decreasing with the landslide magnitude. This study is significant to resolve the conflict between the independence assumptions of the hazard calculation and the conditional attributes in the hazard definition. [ABSTRACT FROM AUTHOR]

Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the role of geotechnical units (i.e., areas with common soil and lithology) is understudied. Indeed, the original SHALSTAB model considers that the whole basin has homogeneous soil. This can lead to the under-or-overestimation of landslide hazards. Therefore, in this study, we aimed to investigate the advantages of incorporating geotechnical units as a variable in contrast to the original model. By using locally sampled geotechnical data, 13 slope-instability scenarios were simulated for the Jaguar creek basin, Brazil. This allowed us to verify the sensitivity of the model to different input variables and assumptions. To evaluate the model performance, we used the Success Index, Error Index, ROC curve, and a new performance index: the Detective Performance Index of Unstable Areas. The best model performance was obtained in the scenario with discretized geotechnical units’ values and the largest sample size. Results indicate the importance of properly characterizing the geotechnical units when using SHALSTAB. Hence, future applications should consider this to improve models’ predictivity.

This research offers a simple approach for landslide risk assessment on a basin-scale in the Quebec Province of Canada, where landslides are common. Landslide inventory and distribution mapping are developed for the Aux Vases Watershed, the most landslide-affected area in the Saguenay region, for a detailed landslide hazard and risk assessment process. The study uses publicly available data from Google Earth images, published literature, Government data sources for topographic maps and landuse information, as well as the 2016 Census Profile of the Saguenay region of Quebec. ArcGIS software is used for spatial data processing and analyzing datasets to develop maps of landslide susceptibility, vulnerability, and lack of coping capacity, and a landslide risk map of the Aux Vases Watershed was developed. Landslide risk assessment is critical for minimizing the loss of life and property from potential landslides in the study area and empowers communities with useful information so they can make informed decisions to mitigate the risk and build coping capacities. In addition, risk reduction policies and strategies, public safety, and future development planning initiatives will benefit from this research.

For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses. [ABSTRACT FROM AUTHOR]

Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially the case in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration of rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matérn covariance function based on the stochastic partial differential equation approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind. [ABSTRACT FROM AUTHOR]

Ground motion scenarios exists for most of the seismically active areas around the globe. They essentially correspond to shaking level maps at given earthquake return times which are used as reference for the likely areas under threat from future ground displacements. Being landslides in seismically actively regions closely controlled by the ground motion, one would expect that landslide susceptibility maps should change as the ground motion patterns change in space and time. However, so far, statistically-based landslide susceptibility assessments have primarily been used as time-invariant.In other words, the vast majority of the statistical models does not include the temporal effect of the main trigger in future landslide scenarios. In this work, we present an approach aimed at filling this gap, bridging current practices in the seismological community to those in the geomorphological and statistical ones. More specifically, we select an earthquake-induced landslide inventory corresponding to the 1994 Northridge earthquake and build a Bayesian Generalized Additive Model of the binomial family, featuring common morphometric and thematic covariates as well as the Peak Ground Acceleration generated by the Northridge earthquake. Once each model component has been estimated, we have run 1000 simulations for each of the 217 possible ground motion scenarios for the study area. From each batch of 1000 simulations, we have estimated the mean and 95% Credible Interval to represent the mean susceptibility pattern under a specific earthquake scenario, together with its uncertainty level. Because each earthquake scenario has a specific return time, our simulations allow to incorporate the temporal dimension into any susceptibility model, therefore driving the results toward the definition of landslide hazard. Ultimately, we also share our results in vector format – a.mif file that can be easily converted into a common shapefile –. There, we report the mean (and uncertainty) susceptibility of each 1000 simulation batch for each of the 217 scenarios. [ABSTRACT FROM AUTHOR]

The vast majority of statistically-based landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that "the past and present are keys to the future". This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake ( M w = 6.5 ) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential coseismic susceptibility patterns over the last century and compress this information into a hazard model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes. [ABSTRACT FROM AUTHOR]

Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that "citrus grove" was a significant factor with a positive coefficient for all models, whereas "forest" was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves. [ABSTRACT FROM AUTHOR]

Indonesia has enormous geothermal potential because most of Indonesia's territory is located in an active volcano route. One of the challenges in developing geothermal energy is that its location is located on the slopes of a Quaternary Volcanoes which is suceptible to landslides. The purpose of the research that has been carried out is to determine the distribution of landslide-hazard areas on the western slopes of the Arjuno-Welerang Volcano. The analysis was performed using the Analytical Hierarchy Process method based on morphological, geological and structural geological parameters. Geomorphological mapping was carried out by contour map analysis, then classified based on morphometry and morphogenesis. Geological mapping uses the principle of volcanostratigraphy, while mapping of geological structures is done by analyzing contour maps made by Digital Evalation Model and field checking. The high and very high landslide hazard zones are scattered along the fault zone, while the very high landslide hazard areas are located in the ancient crater valleys

In Qingchuan County of Sichuan Province, landslide disasters occur in a large number of places and cover a wide range of areas. Early warning of regional landslide disaster is an important means of effective disaster prevention and mitigation, and an early warning model is the core of successful early warning. The traditional regional geological disaster warning model is limited by the lack of big data and analysis methods of the complicated investigation and monitoring mechanism of the landslide in the study areas, and it has some problems, such as limited warning precision and insufficient refinement. In this paper, the training sample set of landslide disaster in Qingchuan County is constructed on the basis of the integrated collation and data cleaning of the results of geological disaster investigation and monitoring and precipitation monitoring. The sample set includes 27 input feature attributes such as geological environment rainfall and 1 output feature attribute, covering the total number of the samples in Qingchuan County in the past 9 years (2010—2018) up to 1826 (613 positive samples, 1213 negative samples). Based on the logistic regression algorithm, the study and training of the sample set is carried out with a 50%-fold cross validation. The Bayesian optimization algorithm is used for model optimization, and the accuracy and model generalization ability of the model are verified by such indicators as accuracy, ROC curve and AUC value. The ROC curve is also known as the “Receiver Operating Characteristic” curve. AUC value represents the area under the ROC curve. The verification results show that the training result model based on logistic regression algorithm is of good accuracy and generalization ability (accuracy 94.3% and AUC 0.980). Finally, it is proposed that in the actual warning of regional landslide, 27 characteristic attributes of each warning unit in the research area are input according to the format of characteristic attributes of training samples, and the pre-learned and trained model is called to output the probability of occurrence of landslide disaster, and the warning level of landslide disaster is segmented according to the output probability. A yellow alert is issued when the output probability P is greater than or equal to 40% and P is less than 60%. An orange alert is issued when the output probability P is greater than or equal to 60% and P is less than 80%. A red alert is issued when the output probability P is greater than or equal to 80%. In the next step, the accuracy of the model will be further verified in the landslide disaster early warning business in Qingchuan county.

Earthquake-induced landslides are considered the third-largest contributor of societal damages caused by an earthquake. Newmark sliding displacement method-based regional seismic landslide hazard assessment is widely used by researchers for identifying vulnerable slopes for a future seismic event. For this purpose, several researchers have proposed regression-based Newmark slope displacement prediction equations based on various ground motion intensity measures and critical acceleration as the slope representative parameter. However, the standard deviation values of these models are significantly high. In this present work, first of its kind, new artificial neural network-based data-driven prediction models for Newmark's sliding displacement are developed. Different combinations of ground motion intensity parameters (PGA, PGV, Ia, and Tm) and the slope's critical acceleration value are employed to predict slope displacement. A total of nineteen prediction models (five scalars and fourteen vectors) have been developed using a dataset containing 13,707 slope displacement data points. The 'efficiency' and 'sufficiency' study of present models reveals that these models exhibit better performance than existing prediction models. A comparative study with existing models shows that the present models are consistent in terms of displacement patterns. The application of the developed prediction model is demonstrated by performing seismic landslide hazard assessment for slopes in Shimla City, India. [ABSTRACT FROM AUTHOR]

The Wenchuan earthquake in 2008 induced thousands of geological hazards. Among them, earthquake-induced landslides are extremely disastrous, causing considerable social and economic losses and damage to the ecology and environment. Therefore, it is of great significance to accurately predict the spatial distribution of earthquake-induced landslides. The research in this paper focuses on ten extremely earthquake-stricken areas of the Wenchuan earthquake; landslide hazard data are collected from the study areas, and the terrain information entropy, distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), peak ground acceleration (PGA) and other landslide conditioning factors are extracted. Combined with a deep neural network (DNN), a landslide hazard spatial prediction model is constructed. Through Dataset 1 and Dataset 2, which are generated randomly, the training dataset (70%) and the validation dataset (30%) are divided to verify the robustness and accuracy of the model, and the landslide susceptibility map of the study area is obtained. Moreover, considering the area under curve (AUC), the impacts of different conditioning factors on the landslide hazard prediction model are analyzed. The research results show that the DNN-based landslide hazard spatial prediction model (AUCMean = 93.66%, RecallMean = 85.70%) has a better prediction performance in the training step. From the validation step, the influences of various factors on the spatial prediction model of landslide hazards established in this paper (from high to low) are as follows: distance to faults, lithology, distance to rivers, PGA, terrain information entropy, NDVI, and distance to roads. The landslide hazard point data and landslide susceptibility map are highly consistent, indicating that the application of a deep learning algorithm in hazard susceptibility assessment is effective and can provide a scientific basis for landslide hazard early warning and disaster prevention and mitigation in mountainous areas prone to earthquake disasters. [ABSTRACT FROM AUTHOR]

An interdisciplinary study has been adopted to investigate the upper Maira Valley (Western Alps, Italy). A geological map of an unmapped area, of about 12 km2, at scale 1:10.000, has been realized. The combination of field surveys, GIS database creation, aerial photo observation, local archival data consultation, geo-structural analysis and drillholes re-interpretation outlined a relationship between structures and landforms. A ductile and brittle deformation history with the definition of four discontinuity systems (F1-F4) has been detected. Where the fracturation is intense, rock-falls and topplings are triggered. In area associated with a homogeneous presence of weathered cover, debris flows were identified. The geo-structural pattern obtained from the surveys in the upper Maira Valley allowed characterizing detachment zones of the slope overlooking Acceglio town. The Gollone Landslide is an excellent case study to unravel the structural-morphological interaction and the kinematic evolution due to its framework.