Natural Disaster Prediction using Machine Learning

Abstract

Natural disasters, such as earthquakes, hurricanes, and droughts, have devastating effects on communities worldwide, but floods are particularly deadly, claiming countless lives and causing extensive damage to infrastructure and livelihoods. To combat this growing threat, we have developed a flood prediction system designed to provide early warnings and mitigate the loss of life and property. Our model is trained on a dataset containing 4,000 records with 23 critical environmental and socio-economic factors, including monsoon intensity, urbanization, and climate change etc. To address this problem, we proposed three efficient machine learning models-Linear Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—to predict flood probability. The models demonstrated impressive accuracy, ranging from 84% for SVM to 99.9% for Random Forest, with Random Forest emerging as the most accurate and reliable model. Moving forward, the system will be enhanced by integrating additional data sources, including satellite imagery, IoT sensor networks, and social media feeds, to improve prediction accuracy and provide a more comprehensive understanding of flood conditions.