Document Type : Original Research Paper
Authors
1 Department of Remote Sensing and GIS, Faculty of Geography, Tehran University, Tehran, Iran
2 Department of Surveying Engineering, Shahid Beheshti University, Tehran, Iran
Abstract
Background and Objectives: Earthquakes, among the most unpredictable and devastating natural disasters, result in significant human casualties and financial losses worldwide each year. Their sudden occurrence and destructive potential categorize them as critical crises that demand efficient and innovative management strategies. Contemporary crisis management practices emphasize three key phases: preparedness (before the event), response (during the event), and recovery (after the event). Among these, rescue operations, which are part of the response phase, play a vital role in saving lives and mitigating further damage. However, given the urgency and complexity of rescue efforts, enhancing their effectiveness through innovative methods is essential. This study introduces a novel approach that leverages spatial intelligence—specifically Geo-Artificial Intelligence (Geo-AI)—to optimize rescue operations in the aftermath of an earthquake.
Methods: This research proposes a Geo-AI–based framework to enhance rescue performance following an earthquake. The approach involves simulating a hypothetical earthquake scenario in Tehran using the Japan International Cooperation Agency (JICA) floating scenario model. A total of 48 rescuers are organized into six teams within the designated study area. These teams are tasked with conducting search and rescue missions facilitated by an augmented intelligent spatial information system. Unlike traditional or manually assigned rescue operations, the proposed model employs reinforcement learning—a subfield of artificial intelligence—to dynamically allocate resources and optimize operational decisions in real-time. The design incorporates a comprehensive set of variables known to influence post-earthquake rescue effectiveness, including team location, response time, victim survivability, and route accessibility.
Main Subjects
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