Enhanced Flood Management Using Global Climate Disaster Database

Abstract

Floods are some of the most devastating climate-induced disasters and require high-end tools for prediction, monitoring, and response. The paper introduces "Global Climate Disaster Database (Case Evidence)" - an integrated platform utilizing deep learning with curated image datasets to better flood management. Combining high-resolution images with real-world case studies, this database can help provide actionable insights for researchers, policymakers, and emergency responders. The system uses MobileNetV2 to achieve an accuracy of 94.36% in real-time flood detection while reducing misclassification by 32% than traditional methods. The potential of the platform includes making detailed visualizations and providing alerts to facilitate decision-making. Large-scale experiments affirm the scalability and efficiency of the platform, which indicates its capability to revolutionize flood management by increasing the precision of prediction and response times. The main applications involve urban flood monitoring, agricultural safeguarding, and disaster preparedness. This paper points out the need for global cooperation and open data sharing to help reduce the severe impacts of climate-driven flooding. Future developments will include expanding the database to encompass other climate-related disasters and include advanced prediction models, which will enhance the robustness and usability of the platform.