Hydrosense: A Machine Learning Based Real-Time Flood Risk Early Warning Framework

Abstract

Flooding remains a pressing global concern, intensified by climate variability, erratic precipitation patterns, and unsustainable management of water systems. This study proposes a predictive approach to flood risk assessment using regression-based machine learning techniques, harnessing both climatic and hydrological variables. The process begins with comprehensive data preprocessing, which includes null value treatment, outlier handling, and refinement of feature sets. Exploratory data analysis through visualizations such as distribution plots and correlation matrices aids in identifying influential predictors. The core modeling phase employs regression algorithms like Linear Regression, enhanced through K-fold cross-validation to validate model stability and generalizability. This framework aims to deliver improved flood forecasting accuracy, supporting early warning systems and informing mitigation strategies to reduce disaster impacts on communities and infrastructure.