Aug 2026· Scientific Reports· Vol 16· 0 citations· 26 references
Medicine
Abstract
This study presents a landslide susceptibility assessment using multiple parameters by integrating remote sensing, GIS, and field observations in the Jammu and Kashmir region. Eight contributing variables were selected for landslide susceptibility analysis: Land Use and Land Cover (LULC), proximity to roads, streams, slope gradient, slope orientation (aspect), geology, geomorphology, and elevation. In addition, an extensive landslide inventory consisting of 669 landslide events was developed using the Field Landslide Inventory Mapping (FLIM) application, LISS-IV satellite data, and field observations, covering an area of 42,950.43 km². Landslide susceptibility mapping (LSM) was carried out using the Analytical Hierarchy Process (AHP) approach and validated with MaxEnt software and field-generated landslide data. The resulting landslide susceptibility map was classified into five categories: very high, high, medium, low, and very low susceptibility zones. Based on the AHP approach, these zones cover 3.65% (1,569.0483 km²), 24.43% (10,492.8912 km²), 51.56% (22,147.2369 km²), 18.81% (8,079.2199 km²), and 1.54% (662.0301 km²) of the study area, respectively. The weighted overlay and MaxEnt models proved effective for landslide vulnerability mapping, with MaxEnt achieving AUC values of 0.82 for training data and 0.807 for testing data, indicating good predictive performance. Field validation further showed that 87% of landslides occurred within high and very high susceptibility zones. The Jackknife test identified road proximity, slope, and stream proximity as the most significant independent variables influencing landslide occurrence. The generated landslide susceptibility map provides important insights for reducing landslide risk and serves as a valuable tool for infrastructure planning, community development, and disaster management in the region.
Landslides are a major geohazard in northern Jordan, especially in the Jerash region, where complex geological conditions, steep terrain, and increased human activity contribute to slope instability. The goal of this study is to assess landslide risk in the Jerash area using an integrated geographic information system (GIS) and analytic hi - erarchy process (AHP) method. Seven conditioning factors were selected based on area features, data availability, and previous research: slope, elevation, aspect, lithology, soil texture, land use/land cover (LULC), and rainfall. These parameters were weighted using AHP pairwise comparisons, yielding a consistency ratio of 0.05, indicat - ing satisfactory reliability of the expert judgments. Then, the weighted thematic layers were combined in a GIS environment to create a landslide susceptibility map divided into low, moderate, and high susceptibility zones. The findings show that slope is important factor in determining landslide incidence in the research area, followed by soil texture and lithology. Areas with steep slopes, clay-rich soils, weak lithological units, heavy agricultural activity, and high rainfall are moderately to highly susceptible to landslides. Spatial validation against documented landslide locations shows a substantial correlation between observed events and anticipated high-susceptibility zones, validating the robustness of the proposed model. The created susceptibility map is useful for land use plan - ning, infrastructure development, and catastrophe risk reduction measures in the Jerash region. Overall, this study demonstrates the usefulness of the GIS-AHP framework for landslide susceptibility evaluation in data-scarce, semi-arid environments and supports its use for long-term hazard management in northern Jordan.
R. AlFukaha, Mohammad Alharahsheh, M. Ibrahim et al.· Ecological Engineering &...· 0 citations
Landslides represent an incessant natural hazard in the mountainous topography of Nepal, where steep slopes, complex geology, intense monsoonal rainfall, and speedily expanding road infrastructure interact to magnify slope instability. This study evaluates anticipated landslide susceptibility along the Beshisahar-Chame mountainous road segment in central Nepal using a geographic information system (GIS) based multi-criteria decision analysis framework. Ten crucial landslide conditioning factors were considered, including slope, aspect, curvature, geology, soil type, land use and land cover, rainfall, drainage density, distance to drainage, and distance to road. The Analytical Hierarchy Process (AHP) was employed to derive relative weights for each factor through pairwise comparisons, confirming consistency of expert judgment. Separate thematic layers were generated from digital elevation models, satellite imagery, published datasets, and landslide inventory in the field, and successively integrated using weighted overlay analysis within a GIS surroundings. The resulting landslide susceptibility map categorises the study area into five zones which are very low, low, moderate, high, and very high susceptibility. Results point that 12.32% of the segment falls within very high susceptibility zones, while 29.23% and 33.55% are considered as high and moderate susceptibility, respectively. Highly susceptible areas are primarily associated with steep, south-facing slopes, weak and fractured lithologies, proximity to roads and drainage networks, and zones of extreme rainfall. The findings spotlight the dominant role of geology, slope gradient, and anthropogenic interventions, particularly unplanned road construction, in prompting landslides. The susceptibility map provides a powerful spatial structure for disaster risk reduction, infrastructure planning, and slope management, aiding informed decision-making for safer road development and sustainable land-use planning in the Himalaya territory.
D. Timilsina, B. R. Joshi, Sundar Adhikari et al.· Journal on Transportation Sy...· 0 citations
The Nilgiris district in Tamil Nadu, India, ranks as one of the most susceptible to landslides in the Western Ghats Mountain ranges due to the rugged terrain, fragile geology and high monsoonal rainfall. The terrain in the study area presents considerable elevation ranges between 91 m and 2634 m above mean sea level. The slope gradients attain as high as 89.8°. This study attempts to evaluate the susceptibility to landslides through the combined approach of the Analytical Hierarchy Process (AHP) and Frequency Ratio techniques. Various factors inSluencing landslides were integrated into the model. These include elevation, slope, aspect, curvature, Hill shade, geology, geomorphology, lithology, lineament density and their distances from roads, rivers and lineaments. Land use/land cover information derived from Landsat-8 satellite images and annual rainfall between 2370 mm and 2850 mm were also used as parameters. According to the AHP model results, the most inSluencing factor was slope with a weight of approximately 0.36, followed by geology with a weight of approximately 0.28. Rainfall and lineament density also showed considerable inSluence. Five susceptibility zones were identiSied as very low, low, moderate, high and very high. Approximately 27-29% of the district area falls within the high to very high susceptibility zones, particularly around Udhagamandalam and Coonoor. Gudalur shows relatively low susceptibility. The model results were validated through the Receiver Operating Characteristic (ROC) curve. The Area under the curve (AUC) values for the AHP model were found to be 0.81, while the FR model results showed a high accuracy with a value of 0.87.
Krishna Reddy Maddikera, Ravi Kumar Gudupudi, Mahesh Babu Kota et al.· International Research Journ...· 0 citations
Landslides are recurrent geomorphic hazards in the Nepal Himalaya, where fragile geology, steep terrain, monsoonal rainfall and expanding road construction increase slope instability. This study assesses landslide susceptibility and relative risk in Tamakoshi Rural Municipality, Dolakha, Nepal, by comparing three GIS-based bivariate models: frequency ratio (FR), Shannon entropy (SE) and statistical information index (SII). A landslide inventory of 121 events was prepared from Google Earth imagery, satellite-image interpretation and field verification, and divided into 70% training and 30% validation subsets. Ten conditioning factors were analysed at 30 m spatial resolution: slope, aspect, curvature, elevation, topographic wetness index, lithology, soil type, land use/land cover, distance from roads and distance from rivers. Model discrimination was evaluated using receiver operating characteristic-area under the curve analysis. Landslides were concentrated on steep, mid-elevation slopes, particularly within 250 m of rivers and 100 m of roads, indicating the influence of fluvial undercutting, road excavation and drainage disturbance. SII produced the highest success and prediction AUC values (0.654 and 0.628), followed by FR (0.645 and 0.623) and SE (0.634 and 0.613), although all three models showed only modest discrimination. An AHP-based exposure–vulnerability index was prepared from settlement, population, road, school, hospital and temple indicators. The resulting relative risk zonation identified approximately 15% of the municipality as high or very high risk, mainly in wards 1, 3 and 5. The findings provide a spatial basis for field prioritization, risk-sensitive land-use planning and local disaster risk reduction.
Nabaraj Bajagain, B. Bisht· American Journal of Applied...· 0 citations
This study presents a GIS-based approach for landslide susceptibility mapping (LSM) along the Hoang Sa street in the Son Tra Peninsula, Da Nang City, Vietnam, using the Modified Frequency Ratio (MFR) and Modified Analytic Hierarchy Process (MAHP) methods. Nine factors, including geology, rainfall, slope, aspect, land use, and distance to road, distance to streams, landslide density, vertical relief were integrated with a landslide inventory of 37 events (2020-2025). Model performance was evaluated using the Receiver Operating Characteristic (ROC) analysis, showing that the MFR model, with an area under the curve (AUC) of 0.85, outperforms the MAHP model (AUC $=0.73$). The landslide susceptibility map derived from the MFR model was classified into five categories: very low (1.32-2.00), low (2.01-2.56), moderate (2.57-3.14), high (3.15-3.67), and very high (3.68-4.93). The findings provide a useful basis for landslide risk assessment and slope management.
Duyen-Anh Huynh-Vo, Ngoc Pham-Van, Nam Phan-Hoang et al.· 2026 11th International Conf...· 0 citations
Landslides happen often and cause serious problems in the Teesta River Basin, threatening buildings, communities, and the environment. This study uses a mapping system called GIS combined with a decision-making method called AHP to evaluate where landslides are likely by combining different types of information into a risk map. The AHP method gives importance to ten factors that affect landslides: slope, rainfall, shape of the land, distance from roads, distance from streams, vegetation health (NDVI), stream power (SPI), direction the land faces, land use, and geology. Several spatial tests, like Nearest Neighbour Analysis, Moran’s I, and hotspot analysis, are used to study how landslide areas are spread out. The model’s accuracy is checked using a test called the ROC curve. The results show that slope, rainfall, and land shape are the most important factors, while land use and geology have less effect. The risk map divides the area into five zones: very low, low, moderate, high, and very high risk. About 18.6% and 7% of the area fall into high and very high risk zones, mostly in the south and west, where landslides often happen on steep slopes, high places, heavy rain, and little vegetation. The very low risk zone covers 36.8% of the area, and the moderate zone covers 18.3%. Targeted safety measures are needed in the high-risk zones. More than 63% of the area is in the low-risk category. The spatial tests confirm that landslides cluster in high-risk areas, shown by Moran’s Index (0.77) and Nearest Neighbour Analysis (0.30). Hotspot analysis points out exact high-risk spots, and the ROC test shows the model predicts landslides fairly well with a score of 0.71. These results offer useful information for managing landslide risks and planning infrastructure in the Teesta River Basin.
Prasanya Sarkar, Debolina Pandit, Shrinwantu Raha et al.· Discover Environment· 0 citations
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