Abstract
Advanced, data-driven methods are required for efficient prediction, monitoring, and management due to the rising frequency and severity of natural catastrophes, which are frequently caused by unforeseen climatic changes. Big data analytics has become a game-changing tool for disaster resilience when combined with artificial intelligence (AI), high-resolution satellite imaging, and Internet of Things (IoT) technologies. With an emphasis on practical uses including flood forecasting, wildfire control, and earthquake damage assessment, this study examines how AI-driven analytics might enhance early warning systems, predictive modelling, and real-time disaster response. Monte Carlo simulation and pixel-level picture segmentation are used to illustrate a particular flood forecast technique. Water areas were precisely segmented by examining satellite pictures taken before and after the storm; 3.46% of water was found before the flood and 25.53% after, indicating severe flooding. Despite low-resolution imagery, the method guarantees accurate area calculation, demonstrating its usefulness for flood event analysis and its wider implications for improving disaster management procedures.
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