Flood Forecasting Model Using KNN Algorithm

Abstract - Floods are among the most devastating and frequent natural disasters, responsible for significant loss of life, widespread property damage, and serious disruption to transportation, communication, and essential infrastructure. With climate change, rapid urbanization, and deforestation contributing to the increasing occurrence and intensity of floods, the need for advanced, data-driven solutions has become more urgent than ever. In response to this growing challenge, a machine learning-based application called FloodCare has been developed to provide real-time flood prediction and risk monitoring.FloodCare leverages the power of artificial intelligence and sensor technology to collect and process critical environmental data in real time. The system utilizes a network of sensors strategically deployed across vulnerable regions to monitor key variables such as precipitation levels, temperature changes, and rising water levels in rivers and catchment areas. This data is then analyzed using the K-Nearest Neighbors (KNN) algorithm, a robust and efficient machine learning model capable of classifying regions into various flood risk categories—including low, moderate, and high risk.The analysis results are displayed through interactive heatmaps and geolocation markers, providing users with clear, easy-to-understand visual cues to identify areas at immediate risk. FloodCare features a user-friendly interface that ensures accessibility for both emergency response teams and the general public, enabling swift, informed decision-making during flood events.By converting complex environmental data into actionable insights, FloodCare enhances community preparedness, strengthens early warning mechanisms, and supports emergency planning and disaster response efforts. Ultimately, this platform plays a crucial role in reducing the social and economic impact of floods, making it a reliable and intelligent solution for modern flood risk management and climate resilience strategies.