Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

• DOI: 10.1007/s40808-023-01912-1
• Publication Date: 2023-12-16T13:01:27Z
• Authors: Jatan Debnath; Dhrubojyoti Sahariah; Nityaranjan Nath; Anup Saikia; Durlov Lahon; Md. Nazrul Islam; Shizuka Hashimoto; Gowhar Meraj; Pankaj Kumar; Suraj Kumar Singh; Shruti Kanga; Kesar Chand
• Keywords: Cartography; Physical geography; Geospatial analysis; Drainage basin; Flood Risk; Operations research; Oceanography; 01 natural sciences; Environmental science; Global Flood Risk Assessment and Management; Engineering; Climate change; Environmental resource management; 0105 earth and related environmental sciences; Water Science and Technology; Global and Planetary Change; Geography; Analytic hierarchy process; Paleontology; Hydrology (agriculture); Geology; FOS: Earth and related environmental sciences; Flood myth; Structural basin; Water resource management; Geotechnical engineering; Hydrological Modeling and Water Resource Management; Archaeology; Environmental Science; Physical Sciences; Global Drought Monitoring and Assessment; Flood Inundation Modeling

Abstract Climate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including Himalayan foothill region India. The Jia Bharali River basin, situated this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to environment local communities. Developing accurate reliable susceptibility models is crucial for effective prevention, management, adaptation strategies. In study, we aimed generate a comprehensive zone model catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, Frequency Ratio model, Fuzzy Logic (FL) Multi-criteria Decision Making based Analytical Hierarchy Process evaluate of basin. results revealed that approximately one-third basin area fell within moderate very high flood-prone zones. contrast, over 50% was classified as low demonstrated strong performance, ROC-AUC scores exceeding 70% MAE, MSE, RMSE below 30%. FL AHP were recommended application among areas similar physiographic characteristics due their exceptional performance training datasets. This study offers insights policymakers, regional administrative authorities, environmentalists, engineers working region. By providing robust research enhances prevention efforts thereby serving vital climate strategy regions. findings also implications disaster risk reduction sustainable development areas, contributing global towards achieving United Nations' Sustainable Development Goals.