Disaster Prediction Using Artificial Intelligence

At the intersection of artificial intelligence and disaster management, this essential guide delivers cutting-edge techniques and real-world case studies to help harness AI for faster, life-saving predictions, real-time monitoring, and optimized emergency response.

The frequency and severity of natural hazards are continuously growing as a result of climate change, population growth, urbanization, and environmental degradation. Effective disaster risk management is a critical component for achieving sustainable development. Artificial intelligence and machine learning have the potential to greatly improve disaster management by utilizing predictive analytics, enabling real-time monitoring, and facilitating effective resource distribution. These technologies assist in anticipating disasters, delivering timely alerts, and streamlining response initiatives. AI can predict, prepare for, and respond to crises with speed and accuracy. This book provides a comprehensive overview of emerging techniques in this vital industry. It discusses different aspects, including hazard mapping, real-time event detection and monitoring, and the prediction of extreme events using AI and machine learning. Real-world case studies demonstrate the role of AI in disaster management, including resource allocation, emergency-service coordination, and logistics optimization. This volume is an essential guide for professionals, researchers, and students, providing a thorough understanding of how artificial intelligence is revolutionizing disaster management, from preparedness and prediction to response and recovery.

Disha Thakur, PhD, is an Assistant Professor of Civil Engineering at the University Institute of Technology. She has published 16 research papers and book chapters in reputed international journals and conference proceedings. Her research interests include geotechnical engineering and solid waste management.

Chander Prakash, PhD, is an Associate Professor in the Department of Civil Engineering at the National Institute of Technology, Hamirpur, India. He has published more than 25 research papers in reputed national and international journals and conference proceedings. His research interests include the application of remotely sensed satellite imagery and GIS in natural resource management, hazard analysis, and the planning and management of development projects.

Sanjay Kumar, PhD, is an Assistant Professor at the University Institute of Technology at Himachal Pradesh University. He has published 24 research papers in reputed national and international journals and 18 research papers in international conference proceedings. His research areas include power systems, renewable energy, network planning, microgrids, optimization, GIS, and machine learning.

Himanshu Sharma, PhD, is an Associate Professor at G.H. Raisoni College of Engineering and Management, Nagpur. He has published more than ten research papers in refereed journals and conference proceedings. His areas of expertise include power electronics, machine learning, and optimization techniques.