Multi-dimensional assessment of flood susceptibility drivers in the urban watershed of Guwahati
Floods are a critical natural disaster causing widespread global devastation, exacerbated by the interaction of natural hydrological phenomena and human activities, particularly in rapidly urbanizing areas like Guwahati, India. The present study systematically examines the influence of sixteen envir...
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| Published in: | Acta geophysica Vol. 73; no. 6; pp. 5815 - 5837 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer International Publishing
01.12.2025
Springer Nature B.V |
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| ISSN: | 1895-7455, 1895-6572, 1895-7455 |
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| Abstract | Floods are a critical natural disaster causing widespread global devastation, exacerbated by the interaction of natural hydrological phenomena and human activities, particularly in rapidly urbanizing areas like Guwahati, India. The present study systematically examines the influence of sixteen environmental and socioeconomic parameters on flood dynamics using a comprehensive modeling framework that combines statistical analysis, advanced machine learning (ML) algorithms, and four feature selection methods: genetic algorithm (GA), Boruta, recursive feature elimination (RFE), and recursive partitioning (Rpart). The application of ML models—support vector machine (SVM) and random forest (RF)—with GA-derived parameters resulted in high predictive performance (accuracy: 0.892; precision: 0.887; recall: 0.868), while deep neural networks (DNNs) integrated with explainable artificial intelligence (XAI) enabled robust sensitivity analysis. The results show a strong positive correlation between the topographic wetness index (TWI) and land use and land cover (LULC) with flooding events. Conversely, factors such as proximity to agricultural land and built-up areas show a negative correlation. LULC, precipitation, distance to built-up areas, and population density were identified as important factors influencing inundation patterns. Furthermore, rainfall, LULC, and proximity to built-up areas emerged as key predictors, with SHAP values highlighting rainfall as the most influential driver of flood risk. Spatial analysis delineated approximately 773 km
2
as very low and 38.47 km
2
as very high susceptibility zones. These findings of the study offer actionable insights for urban planners, policymakers, and disaster management authorities to design targeted mitigation strategies, inform zoning regulations, and prioritize resilient infrastructure development. The approach also contributes to broader urban climate resilience strategies, aligning with SDG 11 and SDG 13 objectives. |
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| AbstractList | Floods are a critical natural disaster causing widespread global devastation, exacerbated by the interaction of natural hydrological phenomena and human activities, particularly in rapidly urbanizing areas like Guwahati, India. The present study systematically examines the influence of sixteen environmental and socioeconomic parameters on flood dynamics using a comprehensive modeling framework that combines statistical analysis, advanced machine learning (ML) algorithms, and four feature selection methods: genetic algorithm (GA), Boruta, recursive feature elimination (RFE), and recursive partitioning (Rpart). The application of ML models—support vector machine (SVM) and random forest (RF)—with GA-derived parameters resulted in high predictive performance (accuracy: 0.892; precision: 0.887; recall: 0.868), while deep neural networks (DNNs) integrated with explainable artificial intelligence (XAI) enabled robust sensitivity analysis. The results show a strong positive correlation between the topographic wetness index (TWI) and land use and land cover (LULC) with flooding events. Conversely, factors such as proximity to agricultural land and built-up areas show a negative correlation. LULC, precipitation, distance to built-up areas, and population density were identified as important factors influencing inundation patterns. Furthermore, rainfall, LULC, and proximity to built-up areas emerged as key predictors, with SHAP values highlighting rainfall as the most influential driver of flood risk. Spatial analysis delineated approximately 773 km2 as very low and 38.47 km2 as very high susceptibility zones. These findings of the study offer actionable insights for urban planners, policymakers, and disaster management authorities to design targeted mitigation strategies, inform zoning regulations, and prioritize resilient infrastructure development. The approach also contributes to broader urban climate resilience strategies, aligning with SDG 11 and SDG 13 objectives. Floods are a critical natural disaster causing widespread global devastation, exacerbated by the interaction of natural hydrological phenomena and human activities, particularly in rapidly urbanizing areas like Guwahati, India. The present study systematically examines the influence of sixteen environmental and socioeconomic parameters on flood dynamics using a comprehensive modeling framework that combines statistical analysis, advanced machine learning (ML) algorithms, and four feature selection methods: genetic algorithm (GA), Boruta, recursive feature elimination (RFE), and recursive partitioning (Rpart). The application of ML models—support vector machine (SVM) and random forest (RF)—with GA-derived parameters resulted in high predictive performance (accuracy: 0.892; precision: 0.887; recall: 0.868), while deep neural networks (DNNs) integrated with explainable artificial intelligence (XAI) enabled robust sensitivity analysis. The results show a strong positive correlation between the topographic wetness index (TWI) and land use and land cover (LULC) with flooding events. Conversely, factors such as proximity to agricultural land and built-up areas show a negative correlation. LULC, precipitation, distance to built-up areas, and population density were identified as important factors influencing inundation patterns. Furthermore, rainfall, LULC, and proximity to built-up areas emerged as key predictors, with SHAP values highlighting rainfall as the most influential driver of flood risk. Spatial analysis delineated approximately 773 km 2 as very low and 38.47 km 2 as very high susceptibility zones. These findings of the study offer actionable insights for urban planners, policymakers, and disaster management authorities to design targeted mitigation strategies, inform zoning regulations, and prioritize resilient infrastructure development. The approach also contributes to broader urban climate resilience strategies, aligning with SDG 11 and SDG 13 objectives. |
| Author | Talukdar, Swapan Naikoo, Mohd Waseem Rahman, Atiqur Shahfahad Ahmed, Ishita Afreen |
| Author_xml | – sequence: 1 givenname: Ishita Afreen surname: Ahmed fullname: Ahmed, Ishita Afreen organization: Department of Geography, Faculty of Sciences, Jamia Millia Islamia, Department of Civil Engineering, Indian Institute of Technology – sequence: 2 givenname: Swapan orcidid: 0000-0001-6680-9791 surname: Talukdar fullname: Talukdar, Swapan organization: Department of Geography, Asutosh College, University of Calcutta – sequence: 3 givenname: Mohd Waseem orcidid: 0000-0001-5779-9325 surname: Naikoo fullname: Naikoo, Mohd Waseem organization: Department of Geography and Disaster Management, School of Earth and Environmental Sciences, University of Kashmir – sequence: 4 surname: Shahfahad fullname: Shahfahad organization: Department of Geography, Institute of Science, Banaras Hindu University – sequence: 5 givenname: Atiqur orcidid: 0000-0002-9001-5059 surname: Rahman fullname: Rahman, Atiqur email: arahman2@jmi.ac.in organization: Department of Geography, Faculty of Sciences, Jamia Millia Islamia |
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| Keywords | Flood susceptibility modeling Machine learning algorithms Guwahati urban watershed Urban flood resilience Feature selection methods Human and natural impacts |
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| SubjectTerms | Accuracy Agricultural land Algorithms Artificial intelligence Artificial neural networks Climate adaptation Deep learning Disaster management Drainage Earth and Environmental Science Earth Sciences Emergency preparedness Environmental risk Explainable artificial intelligence Feature selection Flood risk Flooding Floods Genetic algorithms Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Hydrology Infrastructure Land cover Land use Machine learning Natural disasters Neural networks Parameters Population density Proximity Rainfall Research Article - Hydrology and Hydraulics Resilience Sensitivity analysis Spatial analysis Statistical analysis Storm damage Structural Geology Support vector machines Urban areas Urban climates Urban planning Urban watersheds Watersheds Wetness index Zoning ordinances |
| Title | Multi-dimensional assessment of flood susceptibility drivers in the urban watershed of Guwahati |
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