INTEGRATED ARTIFICIAL INTELLIGENCE AND GEOSPATIAL ANALYTICS FOR INTELLIGENT DISASTER PREDICTION SYSTEMS

Abstract

Natural disasters such as floods, earthquakes, landslides, and hurricanes cause significant damage to human life, infrastructure, and the environment. Accurate and timely prediction of such disasters is essential for effective disaster preparedness and mitigation. Traditional disaster prediction methods often rely on historical records and manual analysis, which may not provide timely or accurate results in dynamic environmental conditions. This study proposes an integrated disaster prediction framework that combines Artificial Intelligence (AI) with Geospatial Analytics to enhance the accuracy and efficiency of disaster forecasting. The system utilizes geospatial data obtained from satellite imagery, remote sensing technologies, geographic information systems (GIS), and environmental sensors to monitor environmental changes and identify potential disaster patterns. Artificial intelligence techniques such as machine learning and deep learning models are employed to analyze large volumes of spatial and temporal data. These models learn complex relationships between environmental factors such as rainfall, terrain elevation, soil moisture, temperature, and seismic activity to predict potential disaster events. Geospatial analytics further enhances the system by mapping and visualizing high-risk areas, enabling authorities to understand disaster-prone regions more effectively. The proposed framework aims to provide early warning alerts, risk assessment, and real-time disaster monitoring, helping decision-makers implement preventive measures and improve emergency response strategies. By integrating AI-driven predictive analytics with geospatial intelligence, the system enhances disaster prediction accuracy, reduces response time, and supports sustainable disaster management planning. Overall, this approach contributes to the development of smart and data-driven disaster management systems capable of improving public safety and minimizing the impact of natural disasters