Comparative GIS-Based Landslide Susceptibility and Relative Risk Assessment Using Frequency Ratio, Shannon Entropy and Statistical Information Index Models
Jul 2026· American Journal of Applied Scientific Research· Vol 12, pp. 81-102· 0 citations· 21 references
Abstract
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.
Landslides pose a significant hazard in the Himalayan region of Nepal due to steep terrain, fragile geology, intense monsoon rainfall, and expanding rural infrastructure. This study evaluates landslide susceptibility in Roshi Rural Municipality, Kavrepalanchowk District, using two GIS-based approaches: the Frequency Ratio (FR) model and the Weighted Overlay Method (WOM) based on the Analytical Hierarchy Process (AHP). Key conditioning factors, including slope, elevation, geology, land use/land cover, rainfall, and proximity to roads and rivers, were analyzed. A landslide inventory from the 2024 extreme rainfall event was used for model training and validation through the Receiver Operating Characteristic (ROC) curve and Area under the Curve (AUC). Results show that the Frequency Ratio (FR) model achieved an AUC value of 0.671, indicating moderate predictive capability. In comparison, the Weighted Overlay Method (WOM) produced an AUC value of 0.572, indicating relatively lower prediction performance. These results suggest that the FR model provides a more reliable representation of landslide susceptibility in the study area.
Landslides are recurrent disasters in Aceh Jaya Regency, driven by steep topography, intense rainfall, and the increasing rate of deforestation. These events frequently disrupt communities and damage critical infrastructure, causing economic losses and the displacement of residents. Therefore, precise landslide susceptibility mapping is crucial for effective disaster-risk mitigation. For this purpose, GIS-based Frequency Ratio (FR) and Analytic Hierarchy Process (AHP) approaches were applied to develop a rapid and operational landslide susceptibility map at the regional scale. The eight causative factors of landslides considered in this study were slope, lithology, lineament density, rainfall, drainage density, elevation, soil type, and land use/land cover (LULC). Causative factors were divided into sub-factors and weighted based on the FR and AHP approaches. The landslide susceptibility maps were categorized into five levels: very low, low, medium, high, and very high. The results show that the high to very high landslide susceptibility categories for FR and AHP were 40% (1,527 km
2
) and 29% (1,090 km
2
), respectively. Validation of the susceptibility map using receiver operating characteristic (ROC) method revealed an area under the curve (AUC) of 0.81 for the FR and 0.75 for the AHP models. Landslide density analysis further confirmed that that both approaches effectively discriminated landslide-prone areas, showing a clear spatial alignment between high-susceptibility classes and observed landslide concentrations. The findings of this study provide a first-order tool for landslide susceptibility assessment, offering critical guidance for planning and implementing targeted mitigation measures in the Aceh Jaya Regency.
Muhammad Haikal Razi, Zahratunnisa Zahratunnisa, N. Aulia et al.· Journal of Umm Al-Qura Unive...· 0 citations
Landslides constitute a significant destructive threat in Nepal's mountainous regions, frequently resulting in fatalities, damage to structures and infrastructure, and disruption of the livelihoods of local inhabitants. This study aims to develop and validate a landslide susceptibility map of the Thotne Khola watershed in the Okhaldhunga District, Nepal. This study used Frequency Ratio (FR), Weights of Evidence (WoE), and Information Value (IV) models to prepare landslide susceptibility maps (LSMs). An inventory map of 234 landslides was created by the interpretation of Google Earth images and fieldwork, thereafter divided into 80% training data and 20% testing data. Fifteen conditioning factors contributing to landslides, including aspect, slope, plan curvature, profile curvature, geology, dominant soil, land cover, rainfall, distance to stream, distance to road, NDVI, relative relief, Sediment Transport Index (STI), Stream Power Index (SPI), and Topographic Wetness Index (TWI), were combined with a training dataset using GIS tools to generate the Landslide Susceptibility Maps (LSMs) for the study area. The area was then classified into five zones of landslide susceptibility: very low, low, moderate, high, and very high. The proportions of the entire area categorized as having very low to very high susceptibility for FR were 21.06%, 20.43%, 20.55%, 19.98%, and 17.99%, respectively; for WoE, they were 13.81%, 25.10%, 29.44%, 24.40%, and 7.25%, respectively; and for IV, they were 11.15%, 22.73%, 29.25%, 28.00%, and 8.87%, respectively. The resulting maps were further confirmed using the area under the curve (AUC) and landslide density index methodologies. The AUC values obtained from three models are 0.909 for FR, 0.902 for IV, and 0.871 for WoE. The result shows that all the models performed strong prediction rates; however, the weight of evidence model has an AUC value less than 0.9. The increased LDI values in the high and very high susceptibility categories further confirm the model's accuracy. The results of this study can assist local government in land use management, disaster preparedness, and mitigation strategies.
Sujit Thapa, B. Bhandari, Mina Thapa et al.· Earth Sciences Research Jour...· 0 citations
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
The active development of the foothill areas of the Almaty agglomeration (Southeastern Kazakhstan) requires reliable methods for landslide susceptibility assessment. This study aims to identify the spatial association between landslide occurrence and selected environmental and anthropogenic factors using the Frequency Ratio (FR) method and geographic information systems. The analysis is based on an inventory of 157 landslides, the SRTM digital elevation model, Landsat imagery, WorldClim climate data, geological maps, and OpenStreetMap data. Ten conditioning factors were analyzed: elevation, slope, aspect, precipitation, lithology, distance to faults, rivers and roads, the Normalized Difference Vegetation Index (NDVI), and land use. FR values were calculated for each factor and integrated to produce a landslide susceptibility map. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the Curve (AUC). The resulting susceptibility map identifies areas with different levels of landslide susceptibility and provides a scientific basis for sustainable land-use planning, engineering-geological investigations, safer infrastructure development, natural hazard assessment, and disaster risk reduction in rapidly developing foothill regions. By supporting risk-informed land-use decisions and targeted mitigation measures, the study contributes to the long-term environmental safety and resilience of the Almaty agglomeration.
Landslides pose a recurring threat to human settlements, infrastructure, and ecosystems in the seismically active Chamoli district of Uttarakhand, India. This study presents a comparative landslide susceptibility assessment using three distinct models: Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy Process (AHP). Twenty-four geo-environmental and anthropogenic conditioning factors were integrated to develop landslide susceptibility maps (LSMs) tailored to the region’s complex terrain. Multicollinearity analysis was conducted to ensure statistical robustness, and model performance was validated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metric. The FR model achieved the highest predictive accuracy (AUC = 0.819), followed by AHP (0.789) and SE (0.594). While FR demonstrated superior data-driven reliability, AHP offered interpretability grounded in expert judgment. Thematic analysis revealed slope, geology, rainfall, proximity to roads, and land use changes as key landslide triggers. Susceptibility zonation showed significant spatial variability across models, with high-risk zones concentrated near road corridors, riverbanks, and deforested slopes. The study underscores the value of methodological triangulation in landslide prediction and recommends FR as a reliable framework for future hazard planning. These findings provide actionable insights for disaster mitigation, infrastructure planning, and sustainable land use management in Himalayan regions.
Sandeep Kunwar, H. Khali, Harsh Kumar et al.· International Journal of Geo...· 0 citations
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