A DATA-DRIVEN DECISION SUPPORT SYSTEM FOR POST-EARTHQUAKE DISASTER MANAGEMENT

Earthquakes remain one of the most destructive natural disasters, posing serious threats to human life, infrastructure, and national economies. Countries located in seismically active zones, such as Turkey, Japan, China, and India, have experienced recurring and devastating earthquakes over the years. The complex nature of post-earthquake scenarios often results in delayed response, misallocation of resources, and preventable secondary losses. This research presents the development of a data-driven Decision Support System (DSS) aimed at enhancing the effectiveness of post-earthquake disaster management. The proposed DSS is designed as a modular architecture that integrates real-time data monitoring, damage assessment, and strategic recommendations. It incorporates a mathematical model that analyzes seismic data, population density, and building infrastructure to predict casualties, injuries, and shelter needs. The model is further enhanced using Multi-Criteria Decision-Making (MCDM) techniques to prioritize rescue operations and resource distribution. The system was tested using data from the 2023 Kahramanmaraş earthquake in Turkey, serving as a real-world validation case. Results show that the casualty prediction model achieved over 98% accuracy, while injury estimations reached 91.39%, and shelter needs were predicted with 98.68% accuracy. MCDM analysis identified Onikişubat as the highest-priority district for emergency response, followed by Dulkadiroğlu and Elbistan. Additionally, a user-friendly web-based DSS interface was developed to facilitate timely decision-making by disaster response authorities. The findings demonstrate the potential of intelligent, data-centric systems to improve the coordination and efficiency of post-earthquake disaster response significantly. The research offers a practical framework for integrating analytical tools into disaster management and provides a foundation for future improvements in crisis response planning and resource allocation.