Real-Time Flood Risk Prediction and Alert System for Indian Districts Using Machine Learning

Abstract

Floods are among the most destructive natural disasters in India, causing extensive loss of life, property, agriculture, and public infrastructure every year. Conventional flood monitoring systems primarily depend on historical observations, manual reporting, or threshold-based warning mechanisms, which often fail to provide timely alerts during rapidly changing weather conditions. The increasing availability of real-time meteorological data and machine learning techniques offers an effective solution for improving flood prediction accuracy and early warning capabilities. This paper presents a Real-Time Flood Risk Prediction and Alert System for Indian Districts Using Machine Learning, an intelligent framework that integrates live weather information, geographical characteristics, and automated notification services to provide district-level flood risk assessment. The proposed system utilizes the K-Nearest Neighbors (KNN) classification algorithm to categorize flood risk into four levels: Low, Moderate, High, and Extreme. Real-time weather parameters, including precipitation, wind speed, soil moisture, snowfall, and river discharge, are collected through OpenWeatherMap and Open-Meteo APIs, while static geographical features such as elevation, slope, soil drainage, historical flood occurrence, and river proximity are incorporated to improve prediction accuracy. The predicted flood risk is visualized through an interactive web dashboard developed using Streamlit and Folium. Furthermore, the system integrates Twilio cloud communication services to automatically deliver SMS alerts and reminder notifications to users residing in flood-prone districts. SQLite is employed to manage user registration and alert history while preventing duplicate notifications within the same monitoring interval. The proposed framework provides an efficient, scalable, and cost-effective approach for real-time flood monitoring and disaster preparedness. Experimental implementation demonstrates that integrating machine learning with live environmental data significantly improves flood risk assessment while enabling faster dissemination of emergency alerts, making the system suitable for practical disaster management applications in India.  Keywords— Flood Prediction, Machine Learning, K-Nearest Neighbors, Disaster Management, Fast2SMS, Streamlit, OpenWeatherMap API, Open-Meteo API, Flood Risk Assessment, Early Warning System.