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Machine Learning Algorithms for Natural Disaster Prediction and Management
Abstract
Natural disasters, such as floods, earthquakes, tsunamis, and landslides, pose significant threats to communities and ecosystems. This investigation explores the application of machine learning (ML) techniques in addressing the challenge. ML, a subset of artificial intelligence, involves creating models and algorithms that enable computers to learn from data, offering accurate disaster predictions without explicit programming. Various ML algorithms, including random forest for flood and wildfire prediction, support vector machine for earthquake forecasting, and decision tree for landslide risk assessment, are employed due to their ability to process complex datasets. Beyond prediction, ML plays a vital role in disaster management, optimizing resource allocation, refining emergency response plans, and enhancing evacuation strategies. Real-world case studies illustrate how ML contributes to mitigating disaster damage, emphasizing its role in proactive measures for disaster prevention and management.
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