Landslide Occurrence Research Articles

Naïve and Semi-Naïve Bayesian Classification of Landslide Susceptibility Applied to the Kulekhani River Basin in Nepal as a Test Case

Naïve Bayes classification is widely used for landslide susceptibility analysis, especially in the form of weights-of-evidence. However, when significant conditional dependence is present, the probabilities derived from weights-of-evidence are biased, resulting in an overestimation of landslide susceptibility. As a solution, this study presents a semi-naïve Bayesian method for landslide susceptibility mapping by combining logistic regression with weights-of-evidence. The utility of the method is tested by application to a case study in the Kulekhani River Basin in Central Nepal. The results show that the naïve Bayes approach with weights-of-evidence overpredicts the posterior probability of landslide occurrence by a factor of about two, while the semi-naïve Bayes approach, which uses logistic regression with weights-of-evidence, is unbiased and has more discriminatory power for landslide susceptibility mapping. In addition, the semi-naïve Bayes approach can statistically distinguish the main factors that promote landslides and allows us to estimate the model uncertainty by calculating the standard error of the predictions.

Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network

Landslide is one of the disasters that often occurs in Indonesia in the East Java Province, especially in Bendungan District, Trenggalek Regency. Analysis of landslide susceptibility in Bendungan District is needed to spatially locate the landslide occurrences. The purpose of this study was to predict landslide events using an artificial neural network. Rainfall, topography, physical soil properties, and land-use were used as the explanatory variables. An analytic hierarchy process approach was applied to determine the weight of the variables. The model satisfactorily classified the landslide hazards with an area under curve of 0.96. The northwest area of the Bendungan District was found to be a region at high risk with rainfall and soil texture as the most influential parts in triggering the landslides.

Predicting Rainfall-Induced Landslides in Muriwai, Auckland: A Comprehensive Multi-Model Approach for Enhanced Risk Management

Landslide studies hold significant importance in understanding and mitigating the risks associated with these geohazards. Particularly in regions like New Zealand, where diverse topography and climatic conditions contribute to frequent landslide occurrences, such studies are crucial. This research focuses on Muriwai, Auckland, a region that has experienced substantial landslide activity triggered by rainfall. The study adopts a multi-model approach, integrating both empirical and process-based models to predict rainfall-induced landslides. This approach leverages the strengths of individual models, including logistic regression, random forest, support vector machines, artificial neural networks, and decision trees, thereby enhancing the robustness and accuracy of predictions. A comprehensive dataset, comprising historical landslide records, climatic data, and terrain and geological data, is used to train and validate these models. The data is spatially aligned within a unified Geographic Information System (GIS) database, ensuring consistency and accuracy in the analysis. The multi-model ensemble provides a probabilistic prediction of landslide occurrence, which is visualized as a landslide susceptibility map. This map serves as a valuable tool for understanding the spatial distribution of landslide risks in Muriwai. The performance of each model and the ensemble is evaluated using several metrics, ensuring the reliability of the predictions. The results are interpreted to understand the influence of different factors, particularly climatic parameters, on landslide occurrences. This study's findings contribute to effective landslide risk management and mitigation strategies in Muriwai, Auckland, and provide valuable insights for similar studies in other regions.

Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets

ABSTRACT The occurrence of collapses and landslides due to underground mining has its unique failure mechanism, especially in the Karst mountainous regions of China. Spaceborne and airborne remote sensing observations provide rapid and effective tools for assessing surface changes and monitoring surface deformation of such landslides. In this study, we take the Jianshanying landslide, a typical mining-induced and fast-deformed landslide, as an example, and reveal the failure mechanism of such landslide by investigating the historical surface displacement. First, the complete evolution of the landslide surface was investigated from its original state to the overall sliding. The data include the satellite and Unmanned Aerial Vehicle (UAV) optical images, UAV three-dimensional (3-D) real scene models, high-resolution Light Detection and Ranging (LiDAR) DEM, and field survey. The results show that the head region entered the high deformation stage after 2019, the maximum deformation rate was 12.3 m/yr. The landslide morphology was formed after the overall slide occurred in September 2020. Then, the pre-event 3-D surface deformation after the landslide entered the high deformation stage was recovered using Interferometric Synthetic Aperture Radar (InSAR), differential DEM, and SAR/optical offset-tracking techniques. The vertical deformation was recovered around −30 m from 2019 to 2020. In particular, we solved the problem of unequal accuracy of SAR and optical offset-tracking observations in 3-D deformation inversion by employing the Helmert variance component estimation method. The maximum deformation was 6 m and 3 m within 4 months in the NS and EW directions, respectively. Finally, we revealed the failure mechanism of the Jianshanying landslide based on the disparity of horizontal and vertical deformation. That is, underground mining causes a significant subsidence of the rear part of the landslide body, resulting in different stress changes in the rear and front parts of the landslide body, which eventually led to sliding of the front part of the slope along the free surface. This work investigates and monitors the typical underground mining-induced Jianshanying landslide by using multi-sensor remote sensing approaches to trace the pre-event surface motions and to reveal its failure mechanism.

Influence of altitude, slope, and waterway characteristics on the occurrence of slow-moving landslides in South Korea

Forest soil sediment disasters, categorized as landslides, slow-moving landslides (land creep), and debris flows, result in destruction of forests and properties, loss of life, and damage to infrastructures. Therefore, it is imperative to evaluate factors that cause slow-moving landslides and establish a national-level hazard information system. In this study, we used geographic information system (GIS) to investigate the difference between landslides and slow-moving landslides by overlapping and analyzing landslide hazard map with slow-moving landslide sites. Slow-moving landslide areas (SLAs) and non-SLAs were characterized to compare the effects of altitude and slope in slow-moving landslide events. Finally, we performed a hydrographical analysis of waterways to compare the SLAs and non-SLAs. Grade 3 slow-moving landslide sites were found to be the most prevalent among all landslide hazard grades (1–5), including those in regions outside the specified grades. The altitude and slope of SLAs were 5 m higher and 1° steeper than those of non-SLAs, respectively. The waterways in non-SLAs (205.4 m) were longer than those in SLAs (85.2 m). Our study highlights the importance of altitude, slope, and waterways as triggers for slow-moving landslides and provides an empirical basis for establishing national hazard information systems for these events.

Landslide Susceptibility Assessment of a Railway Based on GIS Application

This study uses GIS-based maps to identify the potential for landslide movement on the railroad section between Prupuk Station and Purwokerto Station. The preparation was conducted by overlaying various maps obtained by online resource data, including rainfall, slope, geology, soil type, and land use, by using the DVMBG 2004 landslide prediction model. The distribution of landslide susceptibility levels shows that of the overall railway lines, 1.10% was very low, 17.29% was low, 63.16% was medium, 18.25% was high, and 0.21% was very high. The results of this study can be used as a reference for operators to provide information systems, landslide monitoring, and mitigation systems, especially for railroads in areas with high and very high vulnerability for landslides. Detailed conditions of landslide parameters and assessment of landslide occurrence on the location are needed to improve the accuracy of the models.

Investigation of landslide hazard areas in the municipality of Cunha (Brazil) and climate projections from 2024 to 2040

Climate change can modify the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In Brazil, an example of a location with a high occurrence of landslides and with few studies on the subject is the municipality of Cunha, located in the State of São Paulo. In this way, the main aims of this study are twofold: 1) to identify and analyze areas susceptible to landslides and rainfall-induced landslide hazard in the municipality of Cunha; and 2) by using climate projections from the regional model Eta-HadGEM2-ES under RCP 4.5, to predict new extreme events and possible new landslides in this municipality, from 2024 to 2040. The susceptibility map has been prepared using selected physical-biotic environment elements whereas the hazard map has been produced using precipitation data. Using Eta-HadGEM2-ES climate projections under RCP 4.5, for precipitation events, results project the occurrence of 49 days with landslides for the period between 2024 and 2040. The outcomes of this study are considered of extreme relevance for the local community, public authorities, and decision-makers, to raise awareness regarding future urban planning strategies to prevent and mitigate the effects of catastrophes arising from natural hazard-induced disasters.

KEY ROLE OF FOSSIL SEAWATER IN NEOGENE SEDIMENTARY ROCKS FOR LANDSLIDE OCCURRENCES IN THE NORTHERN PART OF CENTRAL JAPAN

The south-western area of Niigata Prefecture has the highest landslide densities in Japan. Saline groundwater emerges from several landslides in this area and is an influential factor in the occurrence of these landslides. The geochemical approaches to groundwaters and application of the electromagnetic surveys for landslide slopes revealed the hydrogeological structure of the large-scale landslides which was located on the outlet of saline waters from deep reservoirs and landslide-prone area where wide zonal distributions of saline water at depths of more than 50–100 m and fresh water at depths of shallower than 50–100 m. It is most likely that the saline groundwater behavior emerging in large-scale landslides is controlled by injections of the geo-pressured saline waters from deep reservoirs. The injection of the geo-pressured waters into the landslide mass likely results in excess pore pressure, which is a possible cause of landslide occurrence. On the other hand, the shallow fresh groundwater is inferred to be meteoric water of origin that replaced the saline groundwater, which likely weakened the shear strength of bedrocks, resulting in landslides. Thus, pore saline water plays an important key role in the occurrence of landslides, both large-scale landslides with low frequency and common landslides.

Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data

Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation.

Assessment of slope-cut landslides along Pokhara-Baglung Highway, Nepal

The study addresses the ramifications of development initiatives in delicate mountainous terrains, unveiling significant economic constraints and multifaceted environmental challenges. It focuses on investigating substantial landslides triggered by excavations along a specified stretch of Nepal's Pokhara-Baglung Highway. This research holds significance for policymakers engaged in devising highway development strategies that mitigate future landslides, minimizing both costs and the toll on life and assets. The investigation encompassed on-site cataloging of landslides, lab test of sampled soils and a structured questionnaire distributed among local residents. This comprehensive approach facilitated a thorough assessment of landslide occurrences and their consequential effects. The identified landslides exhibited a consistent rotational pattern, characterized by abundant quartzite and phyllite rock formations. The predominant soil composition consisted of fine-to-medium sands, exhibiting a Plasticity Index (PI) range of 0.5 to 3, indicating marginal plasticity. Significantly, a substantial portion (70%) of the populace reported tangible impacts from landslides, with about 32% of affected individuals confirming an average agricultural productivity loss of 4330 kg/km2. Statistical analysis using the Chi-square test indicated a uniform impact across various demographic categories, including gender, education, proximity to the affected site, and social caste. Although fluctuations in the region, the observed temporal precipitation consistency over decades suggests as an accelerating rather than primary causative factor for landslides. Thus, principal causes of slope failures predominantly link to inadequately managed bedrock excavations and suboptimal road drainage systems underscoring the necessity for systematic inquiries into soil stability post-slope incisions. These measures are pivotal in guiding the construction and expansion of road networks within Nepal's Himalayan region.

The Open Landslide Project (OLP), a New Inventory of Shallow Landslides for Susceptibility Models: The Autumn 2019 Extreme Rainfall Event in the Langhe-Monferrato Region (Northwestern Italy)

Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset’s suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis.

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT.

Speech-recognition in landslide predictive modelling: A case for a next generation early warning system

Traditional landslide early warnings are based on the notion that intensity-duration relations can be approximated to single precipitation values cumulated over fixed time windows. Here, we take on a similar task being inspired by modeling architectures typical of speech-recognition tasks. We aim at classifying the Turkish landscape into 5 km grids assigned with dynamic landslide susceptibility estimates. We collected all available national information on precipitation-induced landslide occurrences. This information is passed to a Long Short-Term Memory equipped with the whole rainfall time series, obtained from daily CHIRPS data. We test this model: 1) by randomizing the presence/absence data to represent the slope instability over Turkey and over 13 years under consideration (2008–2020) and 2) by assessing the effect of different time windows used to pass the rainfall signal to the neural network. Results show that the inclusion of the full precipitation signal rather than its scalar approximation leads to a substantial increase in prediction power (approximately 20%). This may potentially pave the road for a new generation of speech-recognition-based landslide early warning systems.

From ground motion simulations to landslide occurrence prediction

Ground motion simulations solve wave equations in space and time, thus producing detailed estimates of the shaking time series. This is essentially uncharted territory for geomorphologists, for we have yet to understand which ground motion (synthetic or not) parameter, or combination of parameters, is more suitable to explain the coseismic landslide distribution. To address this gap, we developed a method to select the best ground motion simulation using a combination of Synthetic Aperture Radar Interferometry (InSAR) and strong motion data. Upon selecting the best simulation, we further developed a method to extract a suite of intensity parameters, which we used to analyse coseismic landslide occurrences taking the Gorkha earthquake (M7.8, 25th April 2015) as a reference. Our results show that beyond the virtually unanimous use of peak ground acceleration, velocity, or displacement in the literature, different shaking parameters could play a more relevant role in landslide occurrences. These parameters are not necessarily the product of peak ground motion but are linked to the total displacement, frequency content, and shaking duration; elements too often neglected in geomorphological analyses. This, in turn, implies that we have yet to fully acknowledge the complexity of the interactions between full waveforms and hillslope responses.

Insights into large landslide mechanisms in tectonically active Agadir, Morocco: The significance of lithological, geomorphological, and soil characteristics

Landslide susceptibility assessment is crucial for land use planning, infrastructure development, and hazard mitigation, particularly in tectonically active regions where lithology and soil characteristics significantly influence slope stability. This study aims to evaluate landslide susceptibility in the Agadir Ida-Ou-Tanane province, a tectonically active area, using a hierarchical multicriteria analysis approach. Eight factors influencing landslide occurrence were considered: lithology, slope, LULC, distance to main faults, hypsometry, drainage density, road density, and aspect. Pairwise comparison matrices and expert opinion were employed to determine the relative importance of these factors. The consistency ratio (CR) was calculated to ensure the reliability of the judgments. The resulting landslide susceptibility map reveals a spatial distribution ranging from very low to very high susceptibility zones. Lithology, mainly clay, and limestone formations, emerged as the most influential factor due to the active tectonic context of the study area. The model was validated using observed landslide locations and the Area Under the Curve (AUC) method, yielding a reasonable validation accuracy. The findings indicate that integrating these factors, with a particular emphasis on lithology and soil characteristics, effectively identifies areas prone to landslides, making the resulting map a valuable tool for land use planning and risk management in the Agadir Ida-Ou-Tanane region. This study contributes to understanding landslide susceptibility in tectonically active areas and provides a practical foundation for future research and decision-making. Regularly monitoring and updating the landslide susceptibility map is necessary to ensure its continued relevance and utility for stakeholders.

Hydromechanics – Slope Monitoring in Rainy Season

Rainfall and soil response are necessary to be monitored to have slope characteristics in detecting landslide occurrence. Even though much research has been carried out worldwide for rainfall monitoring, less research has been conducted in Indonesia for slope monitoring. Therefore, this research was conducted to observe the suction, soil moisture content, and rainfall in a silty sand slope. An automatic rain gauge was set on the ground to measure precipitation. Tensiometer and soil moisture content sensors were installed at depths of 0.5 m; 1 m; and 1.5 m from the slope surface. The monitoring was conducted during the peak rainy season from December 2022 to January 2023. The rainfall amount is about 436.6 mm, and the 6 hours of rainfall events contribute a relatively sizeable rainfall amount (about 31%) to the total. The safety factor of the slope is estimated to decrease by 39%-40% due to the rainfall.

On the estimation of landslide intensity, hazard and density via data-driven models

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.

Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data

Machine learning (ML)-based methods of landslide susceptibility assessment primarily focus on two dimensions: accuracy and complexity. The complexity is not only influenced by specific model frameworks but also by the type and complexity of the modeling data. Therefore, considering the impact of factor data types on the model’s decision-making mechanism holds significant importance in assessing regional landslide characteristics and conducting landslide risk warnings given the achievement of good predictive performance for landslide susceptibility using excellent ML methods. The decision-making mechanism of landslide susceptibility models coupled with different types of factor data in machine learning methods was explained in this study by utilizing the Shapley Additive exPlanations (SHAP) method. Furthermore, a comparative analysis was carried out to examine the differential effects of diverse data types for identical factors on model predictions. The study area selected was Cenxi, Guangxi, where a geographic spatial database was constructed by combining 23 landslide conditioning factors with 214 landslide samples from the region. Initially, the factors were standardized using five conditional probability models, frequency ratio (FR), information value (IV), certainty factor (CF), evidential belief function (EBF), and weights of evidence (WOE), based on the spatial arrangement of landslides. This led to the formation of six types of factor databases using the initial data. Subsequently, two ensemble-based ML methods, random forest (RF) and XGBoost, were utilized to build models for predicting landslide susceptibility. Various evaluation metrics were employed to compare the predictive capabilities of different models and determined the optimal model. Simultaneously, the analysis was conducted using the interpretable SHAP method for intrinsic decision-making mechanisms of different ensemble-based ML models, with a specific focus on explaining and comparing the differential impacts of different types of factor data on prediction results. The results of the study illustrated that the XGBoost-CF model constructed with CF values of factors not only exhibited the best predictive accuracy and stability but also yielded more reasonable results for landslide susceptibility zoning, and was thus identified as the optimal model. The global interpretation results revealed that slope was the most crucial factor influencing landslides, and its interaction with other factors in the study area collectively contributed to landslide occurrences. The differences in the internal decision-making mechanisms of models based on different data types for the same factors primarily manifested in the extent of influence on prediction results and the dependency of factors, providing an explanation for the performance of standardized data in ML models and the reasons behind the higher predictive performance of coupled models based on conditional probability models and ML methods. Through comprehensive analysis of the local interpretation results from different models analyzing the same sample with different sample characteristics, the reasons for model prediction errors can be summarized, thereby providing a reference framework for constructing more accurate and rational landslide susceptibility models and facilitating landslide warning and management.

Landslide Susceptibility Mapping Using Multi-Criteria Decision-Making (MCDM), Statistical, and Machine Learning Models in the Aube Department, France

Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.

Absolute dating and evolutionary model of large rock avalanches on Mars: Examples from the Hydraotes Chaos and Tiu Valles region

Landslides are geomorphological features observed on many planetary bodies, formed in a wide range of geological, geomorphological and environmental conditions. Hence, study of their morphological and morphometric characteristics, along with their absolute or relative dating, can improve the understanding of geological and environmental conditions at the time of their emplacement. On Mars, landslides are present over a very large part of the planet, and the most spectacular and best studied are those in Valles Marineris where many mass wasting phenomena occurred between Hesperian and Late Amazonian periods. The present study aims at improving the knowledge of Martian geological and geomorphological evolution including environmental conditions through absolute dating and morphological analysis of selected large landslides outside of Valles Marineris. These mass wasting features were used as proxies for revealing how active the relatively recent (Middle to Late Amazonian) geological environment of Mars was through the information of their trigger timings and degradation histories. The five landslides of our study, which have never been analyzed previously, are located in the region of Hydraotes Chaos and Tiu Valles near the dichotomy boundary. The geomorphological and morphometric analyses show that the studied landslides, classifiable as rock avalanches, exhibit morphological features and mobility similar to those of other terrestrial and Martian rock avalanches. The results obtained from the absolute dating reveal that these landslides occurred within a time range spanning from 835 Ma (±290) to 252 Ma (±100) ago, consistent with occurrences of other landslides in the equatorial region of Mars. The most plausible triggering factors include ground shaking resulting from meteoric impacts, marsquakes and volcanic events related to the evolution of Tharsis and Elysium regions during the Amazonian period. Local resurfacing events acting on the studied landslides are probably related to aeolian deflation and deposition, minor mass wasting events and impact cratering. Finally, the geomorphological characteristics of these landslide bodies and their absolute dating reveal a possible recent (< 250–150 Ma) tectonic activity along the flanks of Tiu Valles and the southern Hydraotes Channel.