A Logical Remote Sensing Based Disaster Management and Alert System Using AI-Assisted Internet of Things Technology

Natural disasters pose significant threats to communities worldwide, necessitating robust and effective disaster management systems. Monitoring and predicting these events rely heavily on advanced sensor technologies and data analysis. In this work, we propose a comprehensive disaster management model that integrates diverse sensor technologies and advanced data analysis methods. At the core of this model is the hybrid neural decision network, which combines the powerful pattern recognition capabilities of neural networks with the clear rule-based decision-making of decision trees. The proposed model utilizes seismic sensors to monitor earthquake dynamics, temperature sensors to detect heatwaves and wildfires, humidity sensors to predict rainfall and floods, and pressure sensors to forecast storms and hurricanes. This hybrid model delivers accurate and interpretable disaster predictions, enabling timely and effective response strategies. The hybrid neural decision network model for accurate, interpretable disaster predictions with an impressive accuracy of 97.89%, forming a robust system for early warnings and effective disaster response.