Abstract: Natural disasters such as floods, earthquakes, and tsunamis pose significant risks to human life and infrastructure. Early warning systems play a crucial role in minimizing the damage caused by such events. This project presents the Machine Learning Disaster Prediction System, an AI-powered platform designed to predict and analyze natural disasters. Using ma- chine learning algorithms, the system predicts the likelihood of disasters based on historical data, weather patterns, and seismic activity. The system is further enhanced by integrating real-time weather data from external APIs, which improves the accuracy of predictions.
The platform features a user assistance system powered by natural language processing (NLP) to identify distress signals and connect users with emergency services. Additionally, an API-based chatbot extracts the latest disaster-related news and alerts, providing users with up-to-date information on current and predicted disasters. The system allows for secure user registration and feedback through OTP-based verification and admin approval processes, ensuring a safe and reliable environment for users.
The project combines Python, Django, and various APIs to create a comprehensive disaster management tool that offers early warnings, facilitates user assistance, and contributes to better disaster preparedness and response.
Keywords: Machine Learning, Disaster Prediction, NLP, Weather API, Chatbot, Disaster Management, Early Warning, Seismic Activity, Flood Prediction, Earthquake Prediction, Tsunami Prediction
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DOI:
10.17148/IJARCCE.2025.14314
