High Susceptibility Areas Research Articles

Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods

Landslide susceptibility assessment (LSA) aims to determine the spatial probability of landslides, reducing the loss caused by future landslides. In order to assess the impact of various negative sample collection strategies on the prediction accuracy of the landslide susceptibility assessment (LSA) model, and to investigate the effectiveness of landslide susceptibility zoning methods. Taking Fengjie County, Chongqing City, China as the study area, this study proposes three negative sample collection strategies based on slope unit, buffer zone, and information value, and combines them with C5.0 decision tree (DT) model respectively to construct an LSA model. Concurrently, the landslide susceptibility indexes (LSIs) were divided using the K-means clustering algorithm and contrasted with the natural breakpoint classification (NBC), quantile classification (QC), equal interval classification (EIC), and geometric interval classification (GIC) methods. The results show that: (1) Rainfall, elevation, and water system are the primary conditioning factors of landslide development in the study area. (2) The accuracy of the negative sample collection strategy based on the slope units on the model training subset and the test subset reached 97.78 % and 92.99 %, respectively, and the AUC values were 0.978 and 0.930, indicating high model accuracy. (3) The zoning effect based on the K-means clustering algorithm was the best, and the predicted very-high and high susceptibility areas were 805.73 km2 and 567.66 km2, respectively, accounting for 19.59 % and 13.80 % of Fengjie County. The very-high and high susceptibility areas had maximum FR values of 3.963 and 1.432, respectively, when compared to other zoning methods. This study can provide a more objective and scientific method for LSA, and the findings can offer more precise decision assistance for risk management and geological disaster prevention.

Research on geological hazard characteristics and susceptibility of the Duku Highway based on SBAS-InSAR and improved spatiotemporal clustering

The high-altitude Duku Highway is characterized by complex terrain changes and frequent geological hazards, which severely impact the lives of local residents and the sustainable development of the regional economy. The lack of understanding of terrain deformation, coupled with scarce foundational observation data, makes it challenging to apply mainstream susceptibility assessment methods such as slope modeling and causality analysis. Consequently, this study utilizes Sentinel-1 A data and employs the SBAS-InSAR technique to extract and analyze the deformation characteristics of 184 hazard areas along the Duku Highway over nearly three years. Furthermore, the Correlation Clustering Evaluation Model is proposed, attributing hazard properties to unsupervised spatial clustering results, thus enabling the study of hazard susceptibility in data-scarce regions without prior knowledge. The results indicate that the SBAS-InSAR coherence is 0.64, with a validation accuracy of 85%. The high, relatively-high, and moderate susceptibility areas account for 24.7%, 17.1%, and 32.6% of the total area, respectively. The rapid uplift of terrain due to plate compression is a major factor leading to frequent hazards in high and relatively-high susceptibility areas. These regions may spontaneously experience cyclic hazards (minimum of 2 months) without extreme external factors. The research findings offer new insights into regional hazards and provide a basis for the sustainable management of highways.

Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China

In recent decades, global warming has significantly altered both the spatial and temporal distribution of rainfall patterns. This change has heightened the risk of rainfall-induced landslides, which are the most prevalent natural disasters in the mountainous regions of southwestern China. These events pose unpredictable and severe threats to the region, making it essential to forecast future rainfall trends and assess how landslide susceptibility will respond to these changes. Understanding these dynamics is crucial for developing effective strategies to mitigate and adapt to the changing rainfall patterns that influence landslides. This study focuses on Sichuan Province, China, and uses annual cumulative rainfall (ACR) as a key dynamic variable to create landslide susceptibility maps (LSMs). The goal is to explore the evolving relationship between rainfall and landslide susceptibility and use future rainfall projections to predict these risks. To achieve this, a historical landslide geospatial database was compiled across five temporal categories: 2000, 2001–2005, 2006–2010, 2011–2015, and 2016–2020. The extreme learning machine (ELM) was applied to generate LSMs for the years 2000 to2020, while an elasticity framework was used to assess how sensitive landslide susceptibility is to rainfall variations. To project future scenarios, a long short-term memory (LSTM) model was employed to project the ACR for 2030, using monthly rainfall data from 2000 to 2020. This projected ACR was then used to estimate future landslide susceptibility. Results showed a marked increase in high landslide susceptibility areas: 5.6 % by 2005, 0.3 % by 2010, 0.2 % by 2015, and 12.9 % by 2020, all relative to the year 2000. The elasticity analysis revealed that from 2000 to 2020, a 1 % change in rainfall would cause an average 1.35 % change in landslide susceptibility. Looking forward to 2030, the projected rise in ACR is expected to lead to a 2.44 % increase in areas of high landslide susceptibility. Multiple validation techniques were applied to ensure reliability and robustness of these findings.

Derivation of Coastal Erosion Susceptibility and Socio-Economic Vulnerability Models for Sustainable Coastal Management in Senegal

Coastal erosion has posed significant challenges to sustainability and socio-economic stability along Senegal’s coastline, leading to substantial infrastructure losses. Using GIS multi-criteria decision analysis (MCDA), two sub-indices were derived for Senegal’s coastal departments: the physical susceptibility (PSI) and the social-economic vulnerability (SVI) to coastal erosion. The integrated coastal erosion vulnerability (ICER) model was derived by their aggregation. A total of 26 criteria were used, 18 for PSI and 8 for SVI. The criteria weighting coefficients of the sub-indices were determined using the analytic hierarchy process (AHP). Validation of the model accuracy was performed using receiver operating characteristic (ROC) curves that were calculated based on a created coastal erosion cadaster and true positive (TP) sites and manually acquired true negative (TN) sites. The accuracy assessment confirmed the consistency of the physical susceptibility model (PSI) and proved that existing coastal erosion sites are within (5) very high susceptibility areas. Through the generated ICER, the coastal departments were divided into areas of (1) very low, (2) low, (3) medium, (4) high and (5) very high vulnerability to coastal erosion. Very high (5) and high (4) classes cover around 31% of the coastal departments, mostly encompassing a narrow coastal strip and low river valleys and mouths. The presented coastal susceptibility and vulnerability maps, with a spatial resolution of 30 m, identified problematic areas in Senegal’s coastal departments and can help decision-makers in the construction of effective coastal zone management and sustainable development.

Evaluation of landslides susceptibility in Southeastern Tibet considering seismic sensitivity

Southeastern Tibet features a complex geological environment and a high incidence of earthquakes. Earthquake-induced chain disasters pose a great threat to engineering construction and public safety in this area, and landslides are among the most frequent postearthquake disasters. To investigate the impact of earthquakes on landslides, this study constructed a comprehensive database for landslide susceptibility analysis based on various factors, including elevation, slope, slope direction, distances from roads and rivers, proximity to faults, land use patterns, rainfall patterns, and seismic parameters. By integrating the frequency ratio (FR) model with the analytic hierarchy process (AHP) model, this work delineated landslide susceptibility zones in southeastern Tibet. Subsequently, the susceptibility zoning layer was overlaid with the magnitude sensitivity layer and validated using ROC curve analysis to identify the earthquake magnitudes that exerted the greatest influence on landslides. Finally, by incorporating the distance from earthquake epicentres into our refined model framework, different monitoring levels for landslide susceptibility zoning were established. The AHP results show the relative importance of the landslide-influencing factors in southeastern Tibet can be ranked as follows: elevation, slope direction, distance from road, land use, distance from river, slope, and rainfall. The ROC values of the landslide models with seismic sensitivities of 1, 2 and 3 are 0.876, 0.883 and 0.877, respectively, indicating that earthquakes of magnitude 4 and above have a great influence on landslides in the study area. Through the overlay of the landslide susceptibility zoning map and the vector map of distance from seismic focal points, a correlation with the distance between landslide-prone areas and seismic focal points is identified. Within the extremely high susceptibility area and within 40 km of the focal point, there are 220 landslide points, accounting for approximately 34 % of the total landslides in the study area. Additionally, 133 landslide points are located in the extremely susceptible area and within 40–80 km of the focal point, representing approximately 20 % of the total landslides in the study area. The susceptible areas were assessed based on grades, resulting in the production of 4 maps depicting different levels of monitoring for landslide-prone areas. These maps are valuable tools for implementing landslide disaster prevention measures within the study area.

Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake

On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher.

Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.

A new inventory and future projections of thermokarst lakes in the permafrost regions of the Qilian Mountains, northeastern Qinghai-Tibet Plateau, China

Thermokarst lakes—lacustrine basins formed by thermokarst processes—are key components of permafrost ecosystems that significantly influence the geomorphology, hydrology, ecology, and the stability of infrastructure, while also contributing to greenhouse gas emissions. High-resolution mapping of thermokarst lakes' current and future distribution presents a considerable challenge. In this study, we leverage a deep learning approach to delineate the boundaries of thermokarst lakes in the permafrost regions of the Qilian Mountains at the northeastern margin of the Qinghai-Tibet Plateau (QTP) using Sentinel-2 imagery, followed by an estimation of future susceptibility to thermokarst lake formation by employing a machine learning method. We identified a total of 2795 thermokarst lakes covering an area of approximately 51.6 km2. Based on future climate scenarios, there will be a significant reduction in the proportion of high-susceptibility areas. Specifically, under the most extreme SSP5–8.5 scenario, the proportion of areas highly susceptible to thermokarst lake formation decreases from the current 6.7 % to 0.01 % in the period 2081–2100. Our comprehensive inventory provides essential data for research on permafrost hydrology and the evaluation and management of water resources, and enhances our understanding of thermokarst lake formation in relation to environmental factors.

Landslide Hazard Analysis Combining BGA-Net-Based Landslide Susceptibility Perception and Small Baseline Subset Interferometric Synthetic Aperture Radar in the Baige Section in the Upper Reaches of Jinsha River

The geological and topographic conditions in the upper reaches of the Jinsha River are intricate, with frequent occurrences of landslides. Landslide Susceptibility Prediction (LSP) in this area is a crucial aspect of geological disaster risk management. This study constructs an LSP model using a Convolutional Neural Network (CNN) based on a Bilateral Aggregation Guidance (BAG) strategy, termed BGA-Net. A comprehensive landslide hazard analysis, integrating static landslide susceptibility zonation with dynamic surface deformation monitoring, was therefore conducted. The study area selected was the upper reaches of the Jinsha River, particularly the site of the Baige landslide. The BGA-Net model was first proposed for LSP generation, achieving an accuracy exceeding 85%, while the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was jointly applied to comprehensively analyze the dynamic geological hazard risk at a regional scale. The final results were presented in a lookup table format and mapped to delineate and grade each risk level. The results show the method is practical, with high feasibility. Compared with traditional machine learning methods, the BGA-strategy-oriented CNN model more effectively differentiated the extremely low- and extremely high-susceptibility areas, enhancing decision-making processes.

Geological hazard susceptibility assessment based on different mathematical statistical models: a case study of Zhejiang province

Geological disaster is a great threat to people's life and property safety, how to predict it reliably and divide it according to the susceptibility of geological disaster is of great significance. Based on the historical collapse data of Zhejiang Province, this paper selects the evaluation indexes of water flow intensity index (SPI), terrain wetness index (TWI), land use type, slope aspect, slope, annual rainfall, river network density, elevation, road density, plane curvature, normalized difference vegetation index (NDVI) and other factors. Based on the frequency ratio model (FR), random forest model (RF) and logistic regression model (LR), the susceptibility zoning of geological disasters in Zhejiang Province is carried out. The results show that: (1) The high and extremely high risk areas in Zhejiang province are mainly distributed near the river system and road network in the high altitude area; (2) In terms of the evaluation effect of comprehensive PR curve, the random forest model has a better evaluation effect than other models; (3) With the increase of the vulnerability level, the proportion of disasters under each model zoning is gradually increasing, indicating that the zoning effect of each model is good; 4) Quzhou city, Jinhua City, Wenzhou city and Shaoxing city in Zhejiang Province account for a large proportion of the high susceptibility areas and above. The results of vulnerability zoning can provide reference for geological hazard prevention and land planning in Zhejiang province.

Determination of the development of settlements above earthquake susceptibility classes in Atakum district (Samsun/Turkey)

It is not possible to predict and prevent earthquakes in advance. Until now only a few seconds of time can be saved with prediction studies. Therefore, the most logical solution to overcome earthquakes with the least damage is to implement risk management plans. One of the most important studies carried out within the scope of these plans is to determine the earthquake susceptibility of the regions and accordingly, to identify the suitable areas for new settlements. The purpose of the study is to evaluate the extent of earthquake susceptibility in Atakum district and analyse its impact on the developing urban area. To determine the susceptibility, Geographic Information Systems (GIS) and Analytical Hierarchy Process (AHP) were used. For the application of the AHP method, 6 main geographical factors and 28 sub-factors including lithology, slope, distance to fault lines, landforms, maximum ground acceleration and soil permeability were analysed. The rate of weight was calculated for all factors and an earthquake susceptibility map was produced by weighted overlay. Then, the urban development process of Atakum district was determined with satellite images. In order to examine the development of the urban area on earthquake susceptibility classes in the last 23 years, Landsat 7 ETM for 2000 and Landsat 8 OLI/TIRS satellite images for 2013 and 2023 were used. According to the results obtained, the residential areas of Atakum city, especially on the coastline, in the embankment areas and on the alluvial plain floors, are located in the high and very high earthquake susceptibility area.

Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies.

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.

PERFORMANCE EVALUATION OF TEMPORAL MODEL AND SPATIAL CORRELATION METHODS FOR PERSISTENT SCATTERER DETERMINATION IN LANDSLIDE PRONE AREAS

Abstract. The Philippines is no stranger to natural hazards. Aside from flooding, landslides emerge as a primary contributor to damage dealt by natural hazards. Traditional methods for landslide monitoring require on-site field measurements, which makes them resource-intensive and time-consuming. Moreover, the frequency of updating of existing landslide hazard maps is limited by resources and manpower. This research introduces the application of remote sensing technology, specifically Persistent Scatter Interferometry (PSI), as a complementary tool for updating hazard maps. PSI enables the identification of stable ground points, eliminating the need for labor-intensive fieldwork and facilitating assessments of potential slope instability. Two distinct PSI processing methods are evaluated in the study; the Temporal Model approach and the Spatial Correlation model approach. Findings reveal that the Temporal Model approach successfully detected 11,647 Persistent Scatterers (PS) points, while the Spatial Correlation approach identified 272,614 PS points. Furthermore, the Spatial Correlation approach demonstrated its capability to detect PS points within high landslide susceptibility areas. Consequently, the results highlight the suitability of the Spatial Correlation approach for detecting PS points, particularly in landslide-prone regions, offering support to local landslide hazard monitoring systems. This research supports the initiative to a more efficient and cost-effective approach to maintaining up-to-date landslide susceptibility maps in the Philippines in enhancing disaster preparedness and mitigation efforts.

Modeling gully initiation by two codeless nonlinear methods: A case study in a small watershed on the Tibetan Plateau

Land and soil resources are scarce in the Tibetan Plateau, and the region is facing ecological pressure from climate warming and increasing human activities. As a major ecological problem, gully erosion is destroying land and soil resources on the Tibetan Plateau, but related research is limited, and susceptibility areas and influencing factors are unclear. Machine learning methods are often applied to study gully initiation susceptibility, but they require a programming foundation. Therefore, the Redui watershed on the southern Tibetan Plateau with severe gully erosion was selected to evaluate the susceptibility and influencing factors of gully initiation through 12 influencing factors including topography, human activity, and underlying surface conditions, and all 2310 gully headcut sites. Two non-code nonlinear modeling methods, the categorical Regression (CATREG) and geographical detector (Geodetector) methods, were first used in the spatial modeling of gully initiation susceptibility. The results showed that the gully initiation susceptibility of the hillslope around the alluvial fan was highest. The very high susceptibility areas of the CATREG model and Geodetector model account for 18.2% and 16% of the total, respectively. The main influencing factors of gully initiation were elevation, relief, and soil type recognized by CATREG, and elevation, human footprint, and soil type recognized by Geodetector. Elevation is the primary factor controlling downstream susceptibility in both models. The primary factors in the upper and middle reaches are soil type and relief identified by CATREG. Human footprint, soil type, and distance to road are primary factors in the upper and middle reaches identified by Geodetector. The explanatory power of elevation, elevation-relief interaction, Geodetector model and CATREG model were 39%, 54%, 46.4% and 73.8%, respectively, at extremely significant levels (P < 0.001), which means that the influencing factors were well considered and that the methods have great application potential in the future.

The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning

Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area of Beichuan County. Eight environmental factors including a digital elevation model (DEM) are extracted to establish a pixel-wise dataset, along with interpreted landslide data. Two landslide susceptibility models were built, each with a deep neural network (DNN) and a support vector machine (SVM) as the learner, and the DNN model was determined to have the best pre-training performance (accuracy = 88.6%, precision = 91.3%, recall = 94.8%, specificity = 87.8%, F1-score = 93.0%, and area under curve = 0.943), with higher parameters in comparison to the SVM model (accuracy = 77.1%, precision = 80.9%, recall = 87.8%, specificity = 73.9%, F1-score = 84.2%, and area under curve = 0.878). The susceptibility model of Beichuan County is then transferred to Mao County (which has no available dataset) to realize cross-regional landslide susceptibility prediction. The results suggest that the model predictions accomplish susceptibility zoning principles and that the DNN model can more precisely distinguish between high and very-high susceptibility areas in relation to the SVM model.

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

Influence of buffer distance on environmental geological hazard susceptibility assessment.

Previous researches seldom studied the selection of buffer distance between geological hazards (positive samples) and non-geological hazards (negative samples), and its reasonable selection plays a very important role in improving the accuracy of susceptibility zoning, protecting the environment and reducing the cost of hazard management. Based on GIS technology and random forest (RF) and frequency-ratio random forest (FR-RF) models, this study innovatively explored the influence of randomly selected non-geological hazard samples outside different buffer distances on the susceptibility evaluation results, with buffer distances of 100 m, 500 m, 1000 m and 2000 m in sequence. The results show that through the confusion matrix and ROC curve test, the accuracy of the model increases first and then decreases with the increase of buffer distance. Both RF and FR-RF models have the highest accuracy when the buffer distance is 1000 m, and the accuracy of the RF model is generally higher than that of the FR-RF model under the same buffer distance. Similar attribute values of positive samples and randomly selected negative samples or "extreme" attribute values of negative samples are the main reasons for the differences in evaluation results of different buffer distances. According to the weight analysis of causative factors, the distance from road, the distance from river and the normalized vegetation index (NDVI) are the main factors affecting the occurrence of hazards. The high and very high susceptibility areas in the study area are mainly distributed on both sides of roads and water systems, which are the key areas for hazard prevention and reduction. The HMC of RF-1000m decreased by 3.55% on average compared with other models. The results of this study improve the accuracy of geological hazard susceptibility assessment, maintain the safety of ecological environment, and provide a scientific basis for the selection of buffer distance index in local and surrounding areas in the future.

Landslide susceptibility mapping using the analytical hierarchy process and the Geographic Information System along the Agadir Ida Ou-Tanane province, Morocco

This study focuses on evaluating the susceptibility to landslides in the Agadir Ida Ou-Tanane province following the significant seismic event on September 8, 2023, employing Geographic Information System (GIS) and Analytical Hierarchy Process (AHP). The region, characterized by tectonic activity and diverse lithology’s such as clay, limestone, and alluvium, coupled with steep slopes, is inherently prone to landslides. Utilizing the AHP methodology, we integrate ten factors influencing landslide vulnerability: lithology, slope, earthquakes zone, rainfall, land cover (LULC), distance from faults, hypsometry, drainage, distance from road, and aspect to map susceptible areas effectively. Pairwise comparison matrices and expert assessments were employed to establish the relative significance of these factors, with the reliability of judgments evaluated through the calculation of the consistency ratio (CR). The resulting map depicting landslide susceptibility illustrates a spatial continuum from very low to very high susceptibility areas. Given the active tectonic setting of the region, lithology and slope emerge as the predominant factors influencing susceptibility. Model validation was conducted using observed landslide occurrences, enhancing the applicability of this mapping for land-use planning and risk management purposes.

Geospatial modeling for enhanced landslide susceptibility mapping in atlas mountains of the northeast of Algeria

This study presents a practical geospatial approach, based on geomatic principles, to create landslide susceptibility maps that meet contemporary landscape and land management priorities. By employing a GIS-based statistical modeling, our methodology seamlessly integrates a wide range of factors including topography, lithology, land use, and precipitation. This comprehensive approach allows for a holistic evaluation of landslide susceptibility. We use two widely recognized multi-criteria techniques, namely the Analytic Hierarchy Process (AHP) and the Fuzzy Logic Ratio (FR), which in result produce two distinct yet complementary landslide susceptibility maps (LSMs). The creation of these LSMs relies on a carefully curated dataset of landslides, collected through rigorous analysis of high-resolution satellite imagery, interpretation of aerial photographs, and extensive fieldwork. Eleven key factors are selected to inform the modeling process. To assess the accuracy of the LSMs, we employ ROC curves, with the FR method demonstrating superior predictive performance, achieving an impressive accuracy rate of 75% compared to the AHP model's 65%. These findings highlight the effectiveness of our approach in identifying high landslide susceptibility areas, providing valuable insights for informed land use planning, hazard mitigation strategies, and rapid emergency response measures. The GIS-based statistical modeling technique showcased in this research provides a robust framework for generating precise landslide susceptibility maps in complex mountainous landscapes. This research makes a significant contribution to the evolving field of geomatics, enhancing landscape resilience and promoting sustainable land management practices.