Integrative Learning Framework for Floods and Landslides Prediction

Abstract

Flooding along with landslides ranks high in terms of destructive natural events. Human safety, built environments, and financial systems face major threats because of them. Rising occurrence and severity, fueled by shifting climates, expanding cities demand faster, more accurate forecasting tools. Conventional methods in hydrology and geology struggle when it comes to modeling intricate links across ecological, atmospheric, and social conditions. Here, a system powered by machine learning emerges as an approach designed specifically for anticipating these dual disasters. Predicting natural disasters like floods and landslides is crucial for managing risks and responding effectively. This paper presents a hybrid deep learning framework that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict both flood events and landslide risk at the same time. The model uses various data sources, including environmental, hydrological, geological, and socioeconomic information, to capture the complex patterns of space and time. A feature-level data fusion strategy combines different inputs into a single representation. The framework handles both classification and regression tasks, allowing for predictions on disaster occurrence and the estimate of their intensity. Testing on a dataset with 39,000 samples shows that this model performs better than traditional machine learning methods, achieving a classification accuracy of 97% and an F1 score of 99%. The regression results are strong as well, with a Root Mean Square Error (RMSE) of 0.34 and an R2 score of 0.89. Additionally, the model has low inference latency, which makes it ideal for real-time early warning systems. These results demonstrate that the proposed framework significantly improves the reliability of predictions for disaster management efforts.