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Acute events of natural origin, spanning atmospheric, biological, geophysical, hydrologic, and oceanographic realms, persistently menace societies globally. Approximately 160 million people annually bear the brunt of these disasters, with certain regions facing disproportionate impacts. The lack of predictability intensifies the challenge, creating intercommunal capacity gaps and amplifying the dire consequences. In an era where natural disasters pose a persistent threat to human societies and the environment, the integration of artificial intelligence (AI) and machine learning (ML) emerges as a tool of hope. Utilizing AI and Machine Learning for Natural Disaster Management delves into the transformative potential of ML in predicting and mitigating the impact of natural calamities. The book begins by demystifying the essence of machine learning, portraying it as an application of artificial intelligence designed to enable systems to learn and improve autonomously. With a focus on real-world applications, the narrative unfolds the profound impact of ML on diverse sectors such as customer service, healthcare, trading, and natural disaster management. Utilizing AI and Machine Learning for Natural Disaster Management provides instances of ML in predicting earthquakes. By leveraging seismic data, AI systems can analyze magnitude and patterns, providing invaluable insights to forecast earthquake occurrences and aftershocks. Similarly, the book unveils the potential of ML in simulating floods by recording and analyzing rainfall patterns from previous years. The predictive power extends to hurricanes, where data on wind speed, rainfall, temperature, and moisture converge to anticipate future occurrences, potentially saving millions in property damage. Topics range from disaster and pandemic management using ML to applying image-based deep learning for natural disaster prediction. Each topic improves the prediction and response mechanisms for natural disasters, exploring the symbiotic relationship between AI, ML, and disaster management. This book is ideal for academics, public and private organizations, managers, and the wider public.
