Artificial Intelligence - Driven Urban Disaster Prediction and Early Warning System Using Multisource Data Fusion

Abstract

The increasing frequency and intensity of urban disasters such as floods, earthquakes, landslides, heatwaves, and industrial accidents have created significant challenges for disaster management authorities and urban planners. Conventional disaster prediction systems often suffer from limited data integration, delayed response capability, and inadequate predictive accuracy in rapidly changing urban environments. This research presents an artificial intelligence–driven urban disaster prediction and early warning system using multisource data fusion to enhance disaster forecasting accuracy, real-time monitoring, and emergency response effectiveness. The proposed framework integrates artificial intelligence algorithms, machine learning techniques, remote sensing data, IoT sensor networks, meteorological information, satellite imagery, and historical disaster records to establish a comprehensive predictive analytics platform. Multisource data fusion techniques are employed to combine heterogeneous datasets and improve situational awareness for identifying potential disaster risks in urban regions. The proposed study employs nonlinear time-history analysis techniques to investigate the seismic response, deformation characteristics, energy dissipation capacity, and damage progression of reinforced concrete structures subjected to varying earthquake intensities.