Flood and Landslide Prediction Using Machine Learning Algorithms

Flood disasters are among the most devastating natural calamities, causing significant loss of life, property damage, and economic disruption worldwide.Traditional flood prediction methods often lack the accuracy and real-time capabilities required for effective disaster preparedness and response.This project presents an advanced machine learning-based flood risk prediction system that integrates multiple data sources to provide accurate, real-time flood risk assessments.The system employs a comprehensive approach combining environmental factors, infrastructure conditions, and real-time weather data to predict flood probability.The research utilizes three primary datasets: flood risk data (50,000 samples), global landslide catalog (11,033 events), and weather station data (16,743 records).Through advanced feature engineering techniques, over 100 meaningful features were extracted and engineered from the raw data.Multiple machine learning algorithms were implemented and evaluated, including Linear Regression, Random Forest, XGBoost, LightGBM, Gradient Boosting, and Neural Networks.The models underwent rigorous hyperparameter tuning and optimization processes.An ensemble approach combining the best-performing models was developed to achieve maximum prediction accuracy.The final optimized ensemble model achieved an accuracy of 98.01%, with an F1-score of 0.9760, RMSE of 0.002133, and R² score of 0.9982.The system successfully identifies high-risk flood scenarios with a false negative rate of only 0.23%, ensuring critical situations are not missed.System is deployed as a production-ready web application featuring a RESTful API backend built with Flask and an interactive frontend with real-time map integration.The application allows users to input location-specific parameters and receive instant flood risk predictions with visual indicators and detailed risk assessments.Key innovations of this research include: (1) hybrid data merging strategy combining geographic and temporal aggregations, (2) composite risk score engineering based on domain knowledge, (3) multilevel ensemble stacking for enhanced accuracy, (4) real-time weather data integration for dynamic predictions, and (5) interactive map-based interface for user-friendly risk visualization.The system demonstrates significant improvements over traditional flood prediction methods in terms of accuracy, real-time capability, and user accessibility.The research contributes to the field of disaster risk management by providing a practical, deployable solution for flood risk assessment that can be utilized by government agencies, emergency management organizations, and the general public.