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Flood Risk Assement Using AHP, Frequency Ratio, Logistic Regression, and Random Forest in Naraipur Municipality
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Author(s): Rubi Chaulagain (Westernal Regional Campus, Nepal), Rekha Paudel (Westernal Regional Campus, Nepal), Fatima Ezzahra El Ghazali (Geosciences, Georesources, and Environment, Semlalia Laboratory, Department of Geology, Faculty of Sciences Semlalia, Cadi Ayyad University, Morocco)and Adil Moumane (Ibn Tofail University, Morocco)
Copyright: 2026
Pages: 28
Source title:
Advancing Environmental Research Through Applied GIS and Remote Sensing
Source Author(s)/Editor(s): Jamal Al Karkouri (Ibn Tofail University, Morocco), Adil Moumane (Ibn Tofail University, Morocco), Abdessamad Elmotawakkil (Ibn Tofail University, Morocco)and Mouhcine Batchi (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3373-6608-1.ch003
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Abstract
Floods, a natural hazard characterized by the overflow of water from its normal riverbed onto dry land, are a recurrent problem. The study area we choose is Narainapur Municipality. This study aims to develop a detailed flood risk assessment using the Analytic Hierarchy Process, Frequency Ratio, Logistic Regression,and Random Forest models. A flood inventory map was created, with 102 flooded and 95 non-flooded points Flood conditioning factors include slope, NDVI, soil type, distance from streams and roads, rainfall, TWI, aspect, and curvature. The Logistic Regression model (AUC = 0.944) achieved the highest AUC value, indicating superior predictive accuracy compared to other models. The Random Forest model (AUC = 0.936) also performed well, followed by the Frequency Ratio model (AUC = 0.855) and the AHP model (AUC = 0.822). These results highlight the effectiveness of machine learning-based models in our study area.The findings of this study provide valuable insights for flood risk management, offering a scientific basis for better planning and decision-making in vulnerable regions.
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