Artificial Intelligent-Based Earthquake Prediction & Mitigation System: A Data Driven Approach

The unpredictability of earthquakes poses significant challenges in disaster preparedness and mitigation. Traditional seismic prediction techniques often fall spredictionhort due to the complex and nonlinear nature of tectonic processes. This research presents an AI-driven earthquake and mitigation framework, leveraging machine learning algorithms to analyze historical seismic data and identify patterns that enhance forecasting accuracy. A Random Forest Regressor was trained using 14,708 earthquake records, incorporating key attributes such as latitude, longitude, depth, magnitude, and temporal features. The model achieved a Mean Absolute Error (MAE) of 0.249 and an $\mathbf{R}^{\mathbf{2}}$ score of 0.073, highlighting both the complexity of seismic forecasting and the potential of AI in recognizing hidden trends. Additionally, we introduce a Natural Disaster Prediction System (NDPS) that integrates real-time alerts, aiding authorities and emergency responders in proactive disaster management. Future enhancements will focus on incorporating deep learning techniques (LSTM, CNN), integrating real-time IoT-based seismic sensors, and refining feature engineering strategies for improved predictive performance. This research contributes to the advancement of AI-driven seismic hazard prediction, supporting more resilient disaster mitigation strategies.