AI and ML in Early Warning Systems for Natural Disasters

Jay Kumar Pandey, Mritunjay Rai, Edris Alam

• Publisher: Bentham Science Publishers
• Year: 2025
• Topic: Technology & Engineering
• eBook ISBN:
  9798898811389
• Subtopic: Civil Engineering
• Index: Technology & Engineering

AI and ML in Early Warning Systems for Natural Disasters bridges the gap between advanced computational models and real-world disaster management practices by highlighting how data-driven intelligence can enhance resilience planning and reduce risks in the face of climate change and extreme environmental events. Beginning with an overview of traditional early warning systems and the limitations they face in accuracy and timeliness The book sheds light on to AI- and ML-driven approaches, detailing predictive analytics, anomaly detection, sensor networks, geospatial data integration, and IoT-enabled monitoring systems. Case studies on earthquake prediction, flood forecasting, cyclone tracking, and wildfire detection illustrate the practical applicability of AI-powered models across diverse contexts. Later chapters examine legal frameworks, ethical considerations, and community-based strategies that ensure responsible, sustainable, and inclusive deployment of these technologies. Key Features Presents AI and ML techniques for predictive analytics, anomaly detection, and risk modeling in disaster scenarios. Demonstrates real-world applications through case studies on earthquakes, floods, cyclones, and wildfires. Explores integration of satellite imagery, remote sensing, and IoT-based sensor networks for real-time monitoring. Assesses legal, regulatory, and ethical frameworks shaping AI use in disaster preparedness. Provides multidisciplinary insights, blending computer science, engineering, and disaster management for resilient community planning.

Table of contents

• Welcome
• Table of Contents
• Title
• BENTHAM SCIENCE PUBLISHERS LTD.
• FOREWORD
• PREFACE
• List of Contributors
• Importance of Artificial Intelligence and Machine Learning in Disaster Detection
• Harnessing AI and Machine Learning for Natural Disaster Management
• Recent Advances in Techniques and Applications for Machine Learning in Disaster Management
• Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection
• Revolutionizing Early Warning Systems for Natural Disasters: Integrating AI and ML-driven Models, Tools, and Platforms
• Harnessing Satellite Imagery, Remote Sensing, and IoT for Real-time Disaster Detection and Monitoring – Floods, Earthquake and Wildlife
• Artificial Intelligence Applications in Disaster Management
• Machine Learning Algorithms for Disaster Detection
• The Role of Law in Shaping AI Development for Effective Natural Disaster Warning Systems
• Application of Fuzzy Artificial Intelligence as a Technique to Find The Relative Desirability of Earthquake
• Advanced Applications of Artificial Intelligence and Machine Learning in Disaster Prediction, Detection, and Mitigation
• Integrating AI and ML into Early Warning Systems: A Solution to Climate Change Challenges