Predicting Disasters
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Predicting Disasters
Author: Kerry Smith
language: en
Publisher: University of Pennsylvania Press
Release Date: 2024-02-27
Japan is a place where powerful earthquakes have occurred more frequently and have caused more harm in the modern era than they have in all but a handful of other locations on the planet. In the twentieth century alone, earthquake disasters in Japan took almost as many lives as they had in all of the country’s recorded history up to that point. Predicting Disasters is the first English-language book to explore how scientists convinced policy makers and the public in postwar Japan that catastrophic earthquakes were coming, and the first to show why earthquake prediction has played such a central role in Japan’s efforts to prepare for a dangerous future ever since. Kerry Smith shows how, in the twentieth century, scientists struggled to make large-scale earthquake disasters legible to the public and to policy makers as significant threats to Japan’s future and as phenomena that could be anticipated and prepared for. Smith also explains why understanding those struggles matters. Disasters, Smith contends, belong alongside more familiar topics of analysis in modern Japanese history—such as economic growth and its impacts, political crises and popular protest, and even the legacies of the war—for the work they do in helping us better understand how the past has influenced beliefs about Japan’s possible futures, and how beliefs about the future shape the present. Predicting Disasters makes relevant elements of Japan’s past more accessible to readers interested in the histories of disaster and scientific communities, as well as to those who want to gain a better understanding of the risk and uncertainty surrounding natural phenomena.
Predicting Natural Disasters With AI and Machine Learning
In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML). This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four ‘R’s - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management. This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations.
Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters
Author: Shailesh Nayak
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-07-16
Lessons learned in the last several years have given clear indications that the prediction and efficient monitoring of disasters is one of the critical factors in decision-making process. In this respect space-based technologies have the great potential of supplying information in near real time. Earth observation satellites have already demonstrated their flexibility in providing data to a wide range of applications: weather forecasting, person and vehicle tracking, alerting to disaster, forest fire and flood monitoring, oil spills, spread of desertification, monitoring of crop and forestry damages. This book focuses on a wider utilisation of remote sensing in disaster management. The discussed aspects comprise data access/delivery to the users, information extraction and analysis, management of data and its integration with other data sources (airborne and terrestrial imagery, GIS data, etc.), data standardization, organisational and legal aspects of sharing remote sensing information.