Machine Learning Based Flood and Landslide Prediction

Floods and landslides are among the most destructive natural disasters, causing significant loss of life, infrastructure damage, and economic disruption. Timely prediction of these events is critical for minimizing their impact and enhancing disaster preparedness. This study presents a machine learning-based approach for predicting floods and landslides by analyzing historical data, weather patterns, and environmental factors. The proposed system leverages various machine learning algorithms, including decision trees, support vector machines, and random forests, to process and classify data from multiple sources, such as rainfall, soil moisture, terrain characteristics, and previous event records. By training the models on large datasets, the system is capable of identifying key indicators and patterns associated with flood and landslide occurrences. The prediction results are used to generate early warning signals, helping authorities take proactive measures to mitigate the effects of these disasters. The effectiveness of the system is demonstrated through comparative performance evaluation, where it outperforms traditional methods in terms of accuracy and reliability. This machine learning-based framework offers a scalable and efficient solution for real-time disaster prediction, providing a valuable tool for improving the resilience of communities at risk of floods and landslides.