Estimation of Flood Hazard Zones of Noa River Basin Using Maximum Entropy Model in GIS

Abstract

This study aims to develop a comprehensive flood hazard map for effective hazard management in the Noa river basin, located in Assam, India, through the integration of Geographic Information System (GIS) tools and a Maximum Entropy (MaxEnt) model. The MaxEnt machine learning algorithm was employed, utilizing eight selected geographic and environmental parameters as predictors to generate the flood hazard map. The accuracy of the generated map was evaluated using the Area Under the Curve (AUC) metric. Key findings of the study identified elevation and slope as critical parameters in the assessment of flood risk. Results revealed that the flood hazard map produced by the MaxEnt model achieved an AUC value of 0.85, indicating high predictive accuracy. The research underscores the significance of flood hazard maps as essential tools for policymakers, enabling the identification of areas vulnerable to severe environmental and economic damage. By providing a reliable and precise assessment of flood-prone zones, this study contributes valuable insights for the formulation of effective flood management strategies and mitigation measures. The implementation of such hazard maps is crucial in enhancing preparedness and resilience against flooding events, ultimately safeguarding lives, property, and infrastructure in the Noa River basin.