FLOOD PREDICTION AND EARLY WARNING SYSTEM USING INTEGRATED MACHINE LEARNING MODELS

Abstract

Floods are among the most devastating natural disasters, causing severe socio-economic and environmental damage worldwide. Accurate flood forecasting is essential for disaster preparedness and mitigation. This research proposes a machine learning-based framework for flood prediction that integrates multiple environmental and hydrological parameters such as rainfall intensity, river water level, soil moisture, and temperature. The system employs ensemble learning techniques combining Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) models to improve prediction accuracy and reliability. Historical flood data and meteorological datasets are preprocessed and analyzed to identify key predictive features. Experimental results demonstrate that the integrated model outperforms traditional statistical and single-algorithm approaches in forecasting flood events. The proposed system provides a scalable, data-driven solution for early warning and real-time flood risk management.