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| Main Authors: | , , , , , , , |
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| Format: | Artículo Open Access |
| Published: |
Wiley
2025
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| Subjects: | |
| Online Access: | https://onlinelibrary.wiley.com/doi/10.1155/adce/5861780 |
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Table of Contents:
- Modeling Landslide Susceptibility and Risk Assessment Using GIS–Based AHP and WoE Methods: A Case Study of Maze and Zenti Catchments, Southern Ethiopia Ephrem Getahun Muralitharan Jothimani Leulalem Shano Zerihun Dawit Hailu Regassa Yonas Oyda Desta Ekaso Bisrat Gissila Advances in Civil Engineering Landslides pose a significant hazard in the Maze and Zenti catchments, Gofa Zone, Southern Ethiopia, where steep terrain, high rainfall, and land‐use changes contribute to slope instability. This study assesses landslide susceptibility (LS) using analytical hierarchy process (AHP) and weight of evidence (WoE) models by integrating 12 factors, including slope, rainfall, land use/land cover (LULC), lithology, soil type, elevation, stream power index (SPI), normalized difference vegetation index (NDVI), topographic wetness index (TWI), aspect, lineament density, and curvature. The final susceptibility maps classify the study area into five categories: very low, low, moderate, high, and very high susceptibility zones. The AHP model results indicate that 52.78% of the study area falls within moderate to very high susceptibility zones, with 18.05% classified as high and 6.96% as very high susceptibility. The WoE model classifies 39% of the total area in high to very high susceptibility zones, with quaternary sediments, clay‐rich soils, and high lineament density regions exhibiting the highest susceptibility levels. Model validation using receiver operating characteristic‐area under the curve (ROC‐AUC) analysis confirms that the WoE model (AUC = 0.843) demonstrates superior predictive accuracy compared to the AHP model (AUC = 0.777). The risk assessment reveals that several settlements, including Tunga, Ula, Zulo, and Kencho Secha, are located in very high susceptibility zones, necessitating immediate intervention through early warning systems, slope stabilization, and relocation planning. Additionally, 63% of agricultural lands fall within high to very high susceptibility zones, posing threats to food security and rural livelihoods. To mitigate landslide risks, engineering measures such as terracing, bioengineering, and afforestation, along with sustainable land‐use planning and improved monitoring systems, should be prioritized. This study highlights the effectiveness of geographic information system (GIS)–based models in LS assessment and provides critical insights for disaster management, infrastructure planning, and environmental conservation. The findings underscore the need for continuous monitoring, hybrid modeling approaches, and integration of climate change projections to enhance future landslide prediction and mitigation efforts. 10.1155/adce/5861780 http://creativecommons.org/licenses/by/4.0/