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"DISASTER MANAGEMENT"
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Risky Cities
Over half the world’s population lives in urban regions, and increasingly disasters are of great concern to city dwellers, policymakers, and builders. However, disaster risk is also of great interest to corporations, financiers, and investors. Risky Cities is a critical examination of global urban development, capitalism, and its relationship with environmental hazards. It is about how cities live and profit from the threat of sinkholes, garbage, and fire. Risky Cities is not simply about post-catastrophe profiteering. This book focuses on the way in which disaster capitalism has figured out ways to commodify environmental bads and manage risks. Notably, capitalist city-building results in the physical transformation of nature. This necessitates risk management strategies –such as insurance, environmental assessments, and technocratic mitigation plans. As such capitalists redistribute risk relying on short-term fixes to disaster risk rather than address long-term vulnerabilities.
eBook
Early warning-based multihazard and disaster management systems
\"This book describes in detail disaster management principles with applications through software and early warning systems. The aim is to introduce the concept of advanced technology for disaster management. Hence, it starts with a basic introduction to disasters and their types. Then it examines these functions by taking into account various factors vulnerable to disaster losses. Finally the results are discussed with the aid of software: OPNET and SAHANA Disaster Management Tool. The application of sensor systems to manage the disaster is also extensively discussed\"-- Provided by publisher.
Book
BIM–GIS Integrated Utilization in Urban Disaster Management: The Contributions, Challenges, and Future Directions
In the 21st Century, disasters have severe negative impacts on cities worldwide. Given the significant casualties and property damage caused by disasters, it is necessary for disaster management organizations and the public to enhance urban disaster management. As an effective method, BIM (Building Information Modeling)–GIS (Geographic Information System) integration can significantly improve urban disaster management. Despite the significance of BIM–GIS integration, there is rarely the adoption of BIM–GIS integration in urban disaster management, which significantly hinders the development of the quality and efficiency of urban disaster management. To enhance urban disaster management and reduce the negative impact caused by disasters, this study is developed to perform a systematic review of the utilization of BIM–GIS integration in urban disaster management. Through the systematic review, the capabilities of BIM–GIS integration in disaster prevention and mitigation, disaster response, and post-disaster recovery are reviewed and analyzed. Moreover, the data acquisition approaches, interoperability, data utilization and analysis methods, and future directions of BIM–GIS integrated utilization in the disaster management process are also discussed and analyzed. Through this study, the public and urban disaster managers can effectively familiarize themselves with and utilize the capabilities of BIM–GIS integration in urban disaster management, thereby improving the urban disaster management efficiency and the survival rate of disaster victims worldwide. For BIM and GIS software developers, this study can support them to familiarize themselves with the methods and trends of BIM–GIS integrated utilization in urban disaster management and thus optimize the development of software for BIM and GIS.
Journal Article
Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices
There has been an unsettling rise in the intensity and frequency of natural disasters due to climate change and anthropogenic activities. Artificial intelligence (AI) models have shown remarkable success and superiority to handle huge and nonlinear data owing to their higher accuracy and efficiency, making them perfect tools for disaster monitoring and management. Accordingly, natural disaster management (NDM) with the usage of AI models has received increasing attention in recent years, but there has been no systematic review so far. This paper presents a systematic review on how AI models are applied in different NDM stages based on 278 studies retrieved from Elsevier Science, Springer LINK and Web of Science. The review: (1) enables increased visibility into various disaster types in different NDM stages from the methodological and content perspective, (2) obtains many general results including the practicality and gaps of extant studies and (3) provides several recommendations to develop innovative AI models and improve the quality of modeling. Overall, a comprehensive assessment and evaluation for the reviewed studies are performed, which tracked all stages of NDM research with the applications of AI models.
Journal Article
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research.
Journal Article
UAV- based Photogrammetry and Geocomputing for Hazards and Disaster Risk Monitoring – A Review
Background
The unraveling of the human-induced climate-change crisis has put to the forth the ability of human-beings to impact the planet as a whole, but the discourse of politics has also emphasized the ability of the human race to adapt and counterweigh the environmental change, in turn increasing the public expectation that one should be able to control nature and its affects. Such cozy and reassured society consequently puts an increasing amount of pressure on hazards assessors, emergency and disaster managers “to get it right”, and not only to save the majority, but to save all. To reach such level of competency, emergency relief teams and disaster managers have to work always faster with an increasing need of high quality, high-resolution geospatial data. This need is being partly resolved with the usage of UAV (Unmanned Autonomous Vehicles), both on the ground and airborne.
Results
In this contribution, we present a review of this field of research that has increased exponentially in the last few years. The rapid democratization of the tool has lead to a significant price reduction and consequently a broad scientific usage that have resulted in thousands of scientific contributions over the last decade. The main usages of UAVs are the mapping of land features and their evolution over time, the mapping of hazards and disasters as they happen, the observation of human activity during an emergency or a disaster, the replacement of telecommunication structures impacted by a natural hazards and the transport of material to isolated groups.
Conclusion
Those usages are mostly based on the use of single UAVs or UAVs as single agents eventually collaborating. The future is most certainly in the ability to accomplish complex tasks by leveraging the multiple platforms possibilities. As an example, we presented an experiment showing how multiple UAV platforms taking imagery together at the same time could provide true 4D (3D in time) of geo-processes such as river-bed evolution, or rockfalls, etc.
Journal Article