Landslide Susceptibility Index Research Articles

Mapping landslide susceptibility in Enfraz to Addis Zemen area Northwestern Ethiopia

The study area (Enfraz to Addis Zemen) is located in northwestern Ethiopia, which frequently experiences landslides, causing damage to farmland, engineering structures, infrastructures, and villages, as well as animal and human fatalities. To manage this catastrophic hazard, a comprehensive GIS-based frequency ratio model (FR) was applied to produce a landslide susceptibility map. In this study, 134 landslides were identified from detailed fieldwork and Google Earth imagery analysis, split into 70% to develop the model and 30% for model validation. The relationship between landslide probability with landslide factor classes of lithology, annual mean rainfall, slope, aspect, curvature, elevation, distance to the river, and land use-land cover was analyzed in a GIS environment. FR model assigns weights to each factor class based on observed frequencies. These weighted factors were summed using a raster calculator to produce landslide susceptibility indexes (LSIs), which were classified into very low, low, moderate, high, and very high susceptibility classes using the natural break classification method. The model’s accuracy and performance were validated using the area under the curve of the receiver operating characteristics curve (ROC), which showed an AUC success rate of 92.2% and a predictive rate of 86.05%. These results confirm that the FR model is effective in landslide susceptibility modeling. The generated map can support decision-makers, urban planners, and researchers in land use planning, landslide mitigation strategies, and future research.

Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas.

Geospatial Analysis of Landslide and Assessing its Risk Along the Swat Motorway, Pakistan

Landslides significantly threaten infrastructure and human lives, highlighting the need for effective landslide susceptibility mapping. This research focuses on creating a map showing landslide susceptibility along the Swat Motorway using Remote sensing (RS) and geographic information system (GIS) methods. The objective is to assess the spatial distribution of landslide-prone areas along the motorway corridor, aiding in informed decision-making for infrastructure development and risk mitigation measures. The study begins by collecting and analyzing relevant geological, geomorphological, and climatic data to identify factors contributing to landslide occurrences. This includes slope gradient, lithology, land cover, rainfall intensity, and land use patterns. Remote sensing data, such as satellite imagery and digital elevation models, are utilized to extract information on land surface characteristics and terrain attributes. GIS-based techniques are employed to integrate and analyze the collected data, employing various spatial analysis tools and algorithms to locate potential landslide locations. The Weighted Overlay Method is applied to assign weights and combine the different factors, generating a landslide susceptibility index. Based on the landslide susceptibility map, this research aims to identify and discuss the areas along the highway that are extremely vulnerable to landslides. After creating a map of the Swat Motorway's landslide susceptibility, numerous zones that are prone to landslides were found. Two of these zones are particularly prone to landslides. The creation of a susceptibility map of landslides provides valuable information for planning land use, management of infrastructure, and hazard mitigation along the Swat Motorway. It identifies high-risk zones prone to landslides, enabling policymakers and engineers to make informed decisions regarding the construction of new roads, slope stabilization, and implementation of early warning systems. The results of this research add knowledge to the already-existing knowledge on susceptibility mapping of landslides, specifically in the context of the Swat Motorway. By utilizing GIS and RS techniques, the study demonstrates an effective and efficient approach for assessing landslide hazards along transportation corridors.

A comparative study of intelligent prediction models for landslide susceptibility: random forest and support vector machine

Colluvial landslides widely developed in mountainous and hilly areas have the characteristics of mass occurrence and sudden occurrence. How to reveal the spatial distribution rules of potential landslides quickly and accurately is of great significance for landslide warning and prevention in the study area. Landslide susceptibility prediction (LSP) modeling provides an effective way to reveal the spatial distribution of regional landslides, however, it is difficult to accurately divide slope units and select prediction models in the processes of LSP modeling. To solve these problems, this paper takes the widely developed colluvial landslides in Dingnan County, Jiangxi Province, China as the research object. Firstly, the multi-scale segmentation (MSS) algorithm is used to divide Dingnan County into 100,000 slope units, to improve the efficiency and accuracy of slope unit division. Secondly, 18 environmental factors with abundant types and clear meanings, including topography, lithology and hydrological environment factors, were selected as input variables of LSP models. Then, a widely representative Support Vector Machine (SVM) and Random Forest (RF) models were selected to explore the difference characteristics of various machine learning models in predicting landslide susceptibility. Finally, the comprehensive evaluation method is proposed to compare the accuracy of various slope unit-based machine learning methods for LSP. The results show that the MSS algorithm can divide slope units in Dingnan County efficiently and accurately. The RF model (AUC = 0.896) has a higher LSP accuracy than that of the SVM model (AUC = 0.871), and the landslide susceptibility indexes (LSI) predicted by the RF model have a smaller mean value and a larger standard deviation than those of the SVM model. Conclusively, the overall performance of RF model in predicting landslide susceptibility is higher than that of SVM model.

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.

Landslide risk assessment by integrating hazards and vulnerability indices in Southeast Bangladesh

Landslide risk assessment (LRA) is crucial to develop sustainable risk reduction and response measures. Although Southeast Bangladesh is prone to landslides, there is insufficient comprehensive investigation that incorporates susceptibility and vulnerability assessments with LRA. This study first calculated the Landslide Susceptibility Index (LSI) using the frequency ratio (FR), verified it using the ROC-AUC curve, and then measured the Social Vulnerability Index (SoVI) using Principal Component Analysis (PCA). Finally, the research integrated the LSI and SoVI map by normalizing and weighting to compute a Landslide Risk Index (LRI) map. The region's LSI reveals 10.19 % very high, 28.10 % moderate, and 13.41 % very low landslide susceptibility. According to the FR model, slope, elevation NDVI, and rainfall predispose to landslides. However, the SoVI revealed 39.3 % of Upazilas experienced medium landslide social vulnerability, which is hazard independent. The LRI statistics classify landslide risk as very high (23 %), high (12 %), moderate (28 %), low (20 %), or very low (17 %) for a total area of 19163.53 km2. A district-wise risk analysis ranks landslide risk as Chattogram > Cox's Bazar > Rangamati > Khagrachhari > Bandarban due to short-term or prolonged rainfall, natural drainage changes, unplanned development, deforestation, hydroelectric plant effects, and population growth. The landslide risk is highest in Teknaf, Ukhia, and Ramu Upazilas and lowest in Thanchi, Rowangchhari, Juraichhari, and Langadu. LSI performed satisfactorily with an AUC-based prediction rate of 81.8 % and success rate curve of 87.0 %. Finally, decision-makers can implement this macro-scale regional landslide risk analysis for southeast hilly Bangladesh for developing sustainable risk reduction schemes.

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

Impacts of bridge constructions on channel dynamics and sediment aggradation: An integrated discussion, West Bengal, India

Channel dynamics, including channel migration, erosion, and deposition, is a significant geomorphological phenomenon. It has been noted that the Gish River exists with its widest extension between 1913 and 2021, causing a bottleneck situation. The section G4 (Gish-4) experienced the most significant squeezing character over time. The catchment landslide-induced sediments, mostly transported in the monsoon by 1st and 2nd order streams, deposited at the beds near the rail and road bridges of the rivers Lish, Gish, and Chel. Landslide susceptibility mapping (LSM) and sediment transport index (STI) maps specify the zones of sediment generation and aggradation. Results of low ‘b” value 0.77 + /−0.04 specify active tectonics and instability of the area. A petrographic analysis of landslide samples and river bed sediments justifies that the landslide connected streams are continuously transported and deposited in the river bed. In the Himalayan foothill rivers like Lish, Gish, and Chel, channel planform dynamics may be the result of landslide occurrences, that induce huge sediments in the rivers and sediment transport index indicates the deposition of the sediments near rail and road bridges in spatio-temporal scale.

GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)

Abstract Landslides and slope processes constitute one of the most frequent natural hazards in valleys near major rivers and mountainous regions. The surface layer, characterized by its relatively loose composition, is prone to sliding due to a combination of distinct natural and human-related factors. Specific sections along the right bank of the Danube River in Smederevo city exhibit significant susceptibility to landslide activation, often leading to substantial material losses and posing a risk to the local population. The initial step in the provided research involves analyzing existing literature and mapping landslides within the study area. The initial analysis covers both natural conditions and anthropogenic activities. The second step includes establishing a geospatial database in the Geographic Information System and generating eight thematic maps. In the third step, different weight coefficients were assigned to the criteria, which facilitated the creation of the Landslide Susceptibility Index using the Best–Worst Method. Subsequently, in the fourth step, a composite map illustrating landslide susceptibility was produced. According to this research, about 4% of the territory of Smederevo, or 19.3 km2, is highly or very highly susceptible to landslides. These localities are located on the right bank of the Danube River and around the Ralja River. Receiver operating characteristic-area under the curve value indicates very high predictive power (approximately 1), thus suggesting the reliability of the used methodology. This visualization of areas highly prone to such occurrences empowers policymakers to implement more effective environmental protection measures and institute sustainable management practices for agricultural parcels in this region. Also, the provided research represents the inaugural integration of advanced remote sensing techniques and interdisciplinary investigations, offering deeper insights into landslide activity in the study area and yielding more comprehensive results.

Landslide hazard assessment for the Batxat area of Vietnam using GIS-based spatial prediction models

Located in the northwest of Laocai province, Batxat district has been frequently affected by natural disasters, including landslides and debris flows. Therefore, landslide hazard assessment (LHA) has been a significant task for planning, economic development, and minimizing human and property damage. For this purpose, landslide hazard maps were established in this study using the Analytic Hierarchy Process (AHP) and the combined Analytic Hierarchy Process - Frequency Ratio (AHP&FR) models. Ten landslide-related factors were selected, including elevation, slope, distance to road, distance to drainage, land use and land cover (LULC), average monthly rainfall, lithology, aspect, distance to fault, and relative relief. Afterwards, the weighted value of landslide-related factors and the landslide susceptibility index (LSI) were determined using the Analytic Hierarchy Process. The Frequency Ratio method was used to calculate the weighted value of factor classes. Two landslide hazard maps were established, and the study area was divided into five hazard zones: very low, low, moderate, high, and very high. The performance of the models was determined using the area under the curve (AUC) of the receiver operating characteristic (ROC), the seed cell area index (SCAI), and the precision of the predicted results (P). The AUC values for the success rate of these models were 0.72 and 0.75, and for the prediction rate were 0.67 and 0.70, respectively. The evaluation results of the models showed that, although both the AHP and combined AHP&FR models have good performance for landslide hazard mapping, the AHP&FR model produces more accurate outcomes than the AHP model.

Assessing the applicability of rainfall index spatial interpolation in predicting landslide susceptibility: A study in Brunei Darussalam

Abstract Rainfall is crucial in meteorology and hydrology, significantly impacting landslide risk assessment. This study focused on evaluating rainfall’s role in determining landslide susceptibility in Brunei Darussalam’s Jalan Kota Batu-Subok and Jalan Jangsak-Tutong regions. Using monthly rainfall data (2008 – 2018) from four weather stations, three spatial interpolation methods inverse distance weighting (IDW), radial basis function (RBF), and global polynomial interpolation (GPI) were assessed. The RBF proved superior in predicting rainfall distribution, evidenced by lower error metrics and higher correlation coefficients. The landslide susceptibility index (LSI) derived from the RBF’s rainfall interpolation showed high accuracy in identifying landslide-prone areas, with success rates between 89 % and 94.3 %, and prediction rates from 85.2 % to 95.9 % across the two areas studied. These findings suggest that the RBF-derived LSI is a reliable tool for landslide risk assessment. However, the LSI’s stability, irrespective of the rainfall data or interpolation method used, indicates that factors like terrain and human activities might have a more significant impact on landslide risks than rainfall alone. This research highlights the importance of considering various factors in landslide risk management and land-use planning, offering valuable insights for policymakers and local authorities.

Quantifying effects of changes in forest age distribution on the landslide frequency in Japan

Landslides are destructive natural disasters that cause human and economic losses. Although many studies report the effects of forest age on landslide susceptibility, especially for shallow landslides, no studies have examined the effects at a national scale. We assumed that temporal variations in the annual number of rainfall-triggered landslides in Japan were determined by variations in rainfall and forest age distribution. By this assumption, this study aimed to quantify the decrease in the frequency of rainfall-induced landslides owing to the increasing maturity of forests in Japan. Data were collated from 21 studies covering 11 sites in three countries that reported a landslide susceptibility index (i.e., frequency ratio or landslide density) and the relation between forest age and the normalized landslide susceptibility index (NLSI) was modeled. Using this relation and the area for each forest age class, the change in landslide susceptibility at a national scale (NLSIJpn) was quantified during 1966–2017. The authors developed generalized linear models (GLMs) using the annual number of landslides as the response variable and the NLSIJpn and a rainfall index for each year as the explanatory variables. The number of rainfall-induced landslides was simulated in the GLMs in 15 scenarios with different forest age distributions and rainfall amounts. The number of landslides in young-age-dominated and middle-age-dominated forests was estimated to be 2.4 and 1.1 times, respectively, that in mature-age-dominated forests. The change in the number of landslides from young-age-dominated to mature-age-dominated forests was larger than that from an increase in the rainfall amount of 20%. We conclude that increasing the maturity of forests greatly reduces landslide frequency in Japan. In a changing climate with potentially threatening increases in rainfall, preserving mature forests is important to avoid amplifying landslide susceptibility on a national scale.

Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%–10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.Graphic abstract

Landslide susceptibility prediction and mapping in Loess Plateau based on different machine learning algorithms by hybrid factors screening: Case study of Xunyi County, Shaanxi Province, China

Machine learning models are widely used for landslide susceptibility prediction (LSP). To better develop a landslide susceptibility prediction model for the Loess Plateau, this study compared different machine-learning models (Logistic regression (LR), support vector machines (SVM), random forests (RF), Naïve Bayes (NB), and multilayer perceptron (MLP)). Using Xunyi County, China as the study area, 1096 landslides were collected and divided into a training set (70%) and a test set (30%). Significant influencing factors were selected from 20 landslide factors using GeoDetector. The relationships between these factors and landslides were determined by normalized frequency ratios. Five trained machine learning models calculated landslide susceptibility indices and generated landslide susceptibility maps. Additionally, the accuracy (ACC), precision (PRE), recall (REC), F1 score, AUC values, LSI distribution characteristics, and overlay analysis of historical landslides with LSM were used to evaluate the performance of the models. The results show that the LSP results of the five models are generally reasonable. The results further indicated that the use of GeoDetector was able to filter out the main factors of eolian landslides, improving the predictive performance of the model and obtaining better LSMs. The RF model had the best overall performance, followed by the SVM, and the MLP model created LSM high susceptibility zones that could contain the highest number of historical landslides. The q-values of factor contributions indicate that elevation, NDVI, rainfall, POI kernel density, and RDLS are the main factors controlling landslides, which need to be paid attention to for landslide control in the Loess Plateau region. The results of this study can provide a reference for the follow-up study of landslide prevention and control, especially landslide susceptibility modeling in the Loess Plateau.

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

Multi - criteria analysis based on GIS and open data resources on the internet to establish the landslide susceptibility area of Lam Dong province

Evaluating the risk of landslides to implement preventative measures and aid in development planning is crucial, particularly in regions prone to such occurrences. Employing a landslide susceptibility map proves beneficial in managing land in landslide-prone zones. In this research, we introduce a GIS technology-based approach along with the multi-criteria analysis (MCA) method, incorporating the analytical hierarchy process (AHP) to determine optimal weight allocation for the criteria influencing landslide risk and compute landslide risk assessment indices. Eight criteria affecting landslide risk were chosen from publicly available data sources on the internet: elevation, slope, terrain moisture index (TWI), precipitation, land cover, normalized difference vegetation index (NDVI), soil type, and drainage density. Subsequently, overlaying simple maps facilitated the calculation of the Landslide Susceptibility Index (LSI), leading to the partitioning of the study area into five levels of landslide risk: very high, high, medium, average, low, and very low, covering approximately 8.6%, 24.3%, 32.0%, 23.9%, and 11.2% of the study area, respectively.

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Uncertainties in landslide susceptibility prediction: Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modeling. To overcome this drawback, this study explores the influence of positional errors of landslide spatial position on LSP uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error. This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example. The 30–110 m error-based multilayer perceptron (MLP) and random forest (RF) models for LSP are established by randomly offsetting the original landslide by 30, 50, 70, 90 and 110 m. The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics. Finally, a semi-supervised model is proposed to relieve the LSP uncertainties. Results show that: (1) The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors, and are lower than those of original data-based models; (2) 70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices, thus original landslides with certain position errors are acceptable for LSP; (3) Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Landslide Susceptibility and Risk Mapping in the Tectonic Ensemble Comprising of Eastern Himalayan Zone, Northeast India and Bhutan using Logistic Regression and Random Forest Techniques

Abstract The mountainous terrain of the East-Northeast tectonic ensemble covers an approximate area of 3,11,420 km2 comprising the Eastern Himalayan zone, Northeast India and Bhutan is prone to mass movements. The increasing trend of landslide occurrences in the last few decades sets loud alarm bells for mapping the landslide hotspot zones in terms of Landslide Susceptibility and Risk. Initially, a landslide inventory of around 9751 landslides has been prepared, of which 6826 (70%) has been randomly picked as training dataset and the remaining 2925 (30%) as the testing dataset. Thereafter, Random Forest (RF) and binary Logistic Regression (LR) based Landslide Susceptibility Zonation (LSZ) have been prepared through twelve predictor layers viz. Slope Angle, Slope Aspect, Slope Curvature, Distance to Drainage, Distance to Lineament, Landform, Surface Geology, Distance to Road, Normalized Difference Vegetation Index (NDVI), Landuse/ Landcover (LULC), Rainfall and Epicentre Proximity. Both the Landslide Susceptibility Index (LSI) maps are divided into five classes, viz. ‘low’, ‘moderate’, ‘high’, ‘very high’ and ’severe’, which are then validated statistically by drawing a comparison with the 30% testing chronological landslide inventory database. The statistical index based accuracy assessment in terms of Area Under the Curve (AUC) exhibits an LR model AUC of 0.776 and RF model AUC of 0.820. However, it has been observed that the RF model strongly correlates with the testing inventory dataset exhibiting that around 46% of the landslide-prone terrain is classified in the ‘high to severe’ zones with 41% inventory landslides occurring in these zones. Integrating the Random Forest (RF) based LSI thematic layer with the socio-economic vulnerability layers like the Number of Households and Population Density have demarcated around 20.78% of the region under ‘very high to severe’ socio-economic risk as convoluted by landslide susceptibility in the terrain. The present findings are expected to be useful in urban development and town planning with appropriate slope management and land-use planning.

Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China

Different types of landslides exhibit distinct relationships with environmental conditioning factors. Therefore, in regions where multiple types of landslides coexist, it is required to separate landslide types for landslide susceptibility mapping (LSM). In this paper, a landslide-prone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area (TGRA), China, was selected as the study area. 733 landslides were classified into three types: reservoir-affected landslides, non-reservoir-affected landslides, and rockfalls. Four landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models (logistic regression, random forest, support vector machine), and a Deep Learning (DL) model. After comparing the models using receiver operating characteristics (ROC), the landslide susceptibility indexes of three types landslides were acquired by the best performing model. These indexes were then used as input to generate the final map based on the Stacking method. The results revealed that DL model showed the best performance in LSM without considering landslide types, achieving an area under the curve (AUC) of 0.854 for testing and 0.922 for training. Moreover, when we separated the landslide types for LSM, the AUC improved by 0.026 for testing and 0.044 for training. Thus, this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps. These maps in turn, can be valuable tools for evaluating and mitigating landslide hazards.