Utilizing Ai And Machine Learning For Natural Disaster Management
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Utilizing AI and Machine Learning for Natural Disaster Management
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. 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.
Internet of Things and AI for Natural Disaster Management and Prediction
In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction. By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.
Evolutionary Artificial Intelligence
This book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2024) held in Malaysia during November 26–27, 2024. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.