Integrating Remote Sensing Technologies for Real-Time Disaster Management, Mitigation, and Decision Support Systems

This study provides a detailed understanding of methodologies on how to apply remote sensing technologies, predictive modeling, DSS in disaster management and mitigation. Using satellite images, aerial maps, and remote sensors, the system gathers data for real-time monitoring of, and real-time assessment of the probability of, natural disasters including hurricanes, earthquakes, floods, and wildfire. Disaster prediction has utilized Random Forest, Support Vector Machine (SVM) and Neural Networks models where accuracy was ranging from ${8 7 \%}$. $8 \%$ and $93.5 \%$. This insight was made possible by the DSS that helped in decision making processes, particularly response to disasters and distribution of resources, optimal evacuation plans. Satellite and drone data provided features for real-time monitoring proving the ability for swift detection, which improved the response effectiveness. The optical, radar, and thermal imaging technologies reached the accuracy of over $94 \%$ in post-earthquake damage assessment, which helped in targeting the rescue operations effectively. The developed system is also highly extensible and can be scaled depending on the chosen picture of future disasters, as well as updated as new solutions in field of remote sensing and machine learning appear. The results provide evidence of the effectiveness of the offered system to improve the corresponding disaster preparedness, response, and recovery for further enabling better practices regarding disaster management and fewer associated consequences.