Predictive Analytics for Disaster Management: AI Models for Earthquake and Flood Forecasting

Conventional disaster prediction systems rely on statistics that compare to historical data, which typically fails to provide sufficient early warnings for disasters like floods and earthquakes. It has developed Disaster Predict-AI, an AI-based forecasting system, to address these problems. It uses Convolutional Neural Networks (CNN) for spatial data analysis and Long Short-Term Memory (LSTM) networks for temporal data analysis, allowing for the assimilation of spatiotemporal data in a unified framework. To provide accurate and timely forecasts, it makes use of real-time satellite imagery, seismic sensors, climate parameters, etc. These two prediction tasks based on adaptive models offer 95.6% and 92.4% accuracy for earthquake and flood prediction, respectively, according to the experimental data, when 12 and 48 hours of lead time are used. By encouraging proactive decision-making, the results confirm Disaster Predict-AI's ability to reduce false positives, enhancing preparedness and reaction to disasters.