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Sustainability
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Innovative Technologies and Strategies in Disaster Management
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Innovative Technologies and Strategies in Disaster Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4766

Special Issue Editors

Dr. Sasha Zhijie Dong

E-Mail Website
Guest Editor
Department of Construction Management, College of Technology, University of Houston, Houston, TX, USA
Interests: supply chain and logistics management; disaster resilience; emergency management
Dr. Erick Mas

E-Mail Website
Guest Editor
International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
Interests: multi-agent systems and agent-based simulation; tsunami simulation; evacuation simulation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to climate change and population growth, disasters are becoming more frequent and severe than before and affecting more people. Currently, since it is a difficult task for humans to react to disasters quickly, efforts have been made to recognize and forecast hazards, assess damage impact, and bounce back (or forward) to a new equilibrium. Creating systems to predict, prevent, and efficiently respond to disasters is becoming an urgent priority. In addition, it is necessary to develop effective management strategies and good practices to strengthen communities and co-create disaster resilience.

 Recent technological developments include but are not limited to wireless sensor networks, drones, mobile phones, digital twins, the Internet of Things, artificial intelligence, big data, machine learning, and social media. These technologies are essential in disaster management, enhancing prediction, assessment, and response. Furthermore, since disaster information and data are usually widely distributed and owned by different agencies, efficiently organizing and sharing resources becomes troublesome.

Thus, this Special Issue aims to encourage scholars and experts to report on strategies for effectively using innovative technologies in any or all stages of the disaster cycle. This Special Issue seeks to tackle the barriers encountered in utilizing innovative technologies and contribute to discussing and designing policy implications that may improve disaster risk reduction.

Proposed manuscripts are expected to significantly contribute to the literature and provide specific practical support to disaster relief agencies based on solid research and good-quality data.

Dr. Sasha Zhijie Dong
Dr. Erick Mas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • disaster management
  • emerging technologies
  • data exchange
  • policy implications

Published Papers (3 papers)

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Research

28 pages, 7023 KiB  
Article
Intelligent-Technology-Empowered Active Emergency Command Strategy for Urban Hazardous Chemical Disaster Management
by Jieyin Lyu, Shouqin Zhou, Jingang Liu and Bingchun Jiang
Sustainability 2023, 15(19), 14369; https://doi.org/10.3390/su151914369 - 29 Sep 2023
Viewed by 1179
Abstract
Urban safety production is a core component of social safety and is associated with the production, storage and transport of hazardous chemicals, which are potential sources of disaster in an urban area. Chemicals’ locations in a city present a hidden site of danger, [...] Read more.
Urban safety production is a core component of social safety and is associated with the production, storage and transport of hazardous chemicals, which are potential sources of disaster in an urban area. Chemicals’ locations in a city present a hidden site of danger, which can easily become disaster sites if supervision is inadequate. Aiming to improve the processes and typical scenarios of the production, storage, transportation and use of hazardous chemicals, this paper proposes an intelligent-technology-empowered active emergency command strategy (ITAECS) for urban hazardous chemical disaster management (UHCDM) in smart–safe cities. This paper aims to provide a strategy for active emergency command that takes into account the disaster source; hidden danger site; or disaster site of hazardous chemicals such as natural gas, gasoline and hydrogen energy based on five aspects: intelligent perception technology and equipment, a dynamically perceived IoT system, the accurate deduction of disaster posture, virtual reality emergency rescue rehearsal and an immersive emergency command platform. This research is conducive to the safety, efficiency and greenness of the whole industrial chain, such as the production, storage, transportation, operation and use of hazardous chemicals. There are difficulties and challenges in introducing ITAECS to urban hazardous chemical production safety and emergency management, such as the need for joint promotion of enterprises, industries and governments; uneven technological development; and several scientific–technological issues to be solved, as well as non-uniform standards. Overall, this paper helps improve the emergency management of urban hazardous chemical safety production. Full article
(This article belongs to the Special Issue Innovative Technologies and Strategies in Disaster Management)
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21 pages, 6965 KiB  
Article
Increasing Trends of Heat Waves and Tropical Nights in Coastal Regions (The Case Study of Lithuania Seaside Cities)
by Inga Dailidienė, Inesa Servaitė, Remigijus Dailidė, Erika Vasiliauskienė, Lolita Rapolienė, Ramūnas Povilanskas and Donatas Valiukas
Sustainability 2023, 15(19), 14281; https://doi.org/10.3390/su151914281 - 27 Sep 2023
Viewed by 1278
Abstract
Climate change is leading to an annual increase in extreme conditions. Public health is closely related to weather conditions; hence, climate change becomes a major factor concerning every-day human health conditions. The most common extreme natural phenomenon that affects people’s health is the [...] Read more.
Climate change is leading to an annual increase in extreme conditions. Public health is closely related to weather conditions; hence, climate change becomes a major factor concerning every-day human health conditions. The most common extreme natural phenomenon that affects people’s health is the summer heat wave. During the 21st century, as the air temperature continues to rise, the sea surface temperature (SST) rises along with it, especially along the seacoasts. More massive water bodies, such as seas or larger lagoons, that warm up during the day do not allow the ambient air to cool down quickly, causing the air temperature to often be warmer at night in the coastal area than in the continental part of the continent. Currently, not only an increase in the number of days with heat waves is observed, but also an increase in the number of tropical nights in the coastal zone of the Southeastern Baltic Sea. In this work, heat waves are analyzed in the seaside resorts of Lithuania, where the effects of the Baltic Sea and the Curonian Lagoon are most dominant. Full article
(This article belongs to the Special Issue Innovative Technologies and Strategies in Disaster Management)
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17 pages, 386 KiB  
Article
Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data
by Zhenlong Jiang, Yudi Chen, Ting-Yeh Yang, Wenying Ji, Zhijie (Sasha) Dong and Ran Ji
Sustainability 2023, 15(10), 8035; https://doi.org/10.3390/su15108035 - 15 May 2023
Cited by 1 | Viewed by 1265
Abstract
Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed [...] Read more.
Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed nationwide random telephone surveys to gauge the public’s attitudes and actions towards disaster preparedness. However, these surveys may lack generalizability in certain affected locations due to low response rates or areas not covered by the survey. To address this issue and enhance the comprehensiveness of disaster preparedness assessments, we develop a framework that seamlessly integrates machine learning and simulation. Our approach leverages machine learning algorithms to establish relationships between public attitudes towards disaster preparedness and demographic characteristics. Using Monte Carlo simulation, we generate datasets that incorporate demographic information of the affected location based on government-provided demographic distribution data. The generated dataset is then input into the machine learning model to predict the disaster preparedness attitudes of the affected population. We demonstrate the effectiveness of our framework by applying it to Miami-Dade County, where it accurately predicts the level of disaster preparedness. With this innovative approach, relief agencies can have a clearer and more comprehensive understanding of public disaster preparedness. Full article
(This article belongs to the Special Issue Innovative Technologies and Strategies in Disaster Management)
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