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Advances in Disaster Risk Sciences in the Era of Big...
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Advances in Disaster Risk Sciences in the Era of Big Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4763

Special Issue Editors

Prof. Dr. Zhao Zhang

E-Mail Website
Guest Editor
School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China
Interests: remote sensing application; crop modeling; crop production; climate change assessment; climate risk management; agricultural insurance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dengpan Xiao

E-Mail Website
Guest Editor
College of Geography Science, Hebei Normal University, Shijiazhuang 050024, China
Interests: crop model; phenology; climate change; agricultural water management; agricultural ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Kai Liu

E-Mail Website
Guest Editor
1. Academy of Disaster Reduction and Emergency Management, Beijing 100875, China
2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Interests: disaster reduction; structural dynamics

Special Issue Information

Dear Colleagues,

Under the background of global warming, natural hazards have severely threatened—will continually and inevitably threaten—human lives and properties throughout the world. To accurately and rapidly quantify the risks from such intensified natural hazards, new methods are highly required related to three aspects including hazard, exposure and vulnerability analyses. Fortunately, Big Data is offering a good opportunity for discovering new knowledge and providing powerful tools to accurately quantify the integrated risks from natural hazards. Such multi-data come from all kinds of aerial and earth observations, simulation models (e.g. global climate models), social mediums, professional studies, and etc. How to apply and what type to select are still a big challenge for risk analysis.

Therefore, the special issue aims to encourage researchers to address the recent progresses in the field of disaster risk sciences, fully taking advantage of the new opportunities from Big Data in topics including, but not limited to, the following:

  1. Advanced theoretical and methodological issues on quantifying disaster risks;
  2. Every progress related to hazard, exposure and vulnerability analyses;
  3. Disaster risk development, communication, transition, and governance.

Prof. Dr. Zhao Zhang
Prof. Dr. Dengpan Xiao
Dr. Kai Liu
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. Applied Sciences 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.

Published Papers (3 papers)

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Research

22 pages, 6534 KiB  
Article
A Novel Risk Assessment for Cable Fires Based on a Hybrid Cloud-Model-Enabled Dynamic Bayesian Network Method
by Shenyuan Gao, Guozhong Huang, Zhijin Xiang, Yan Yang and Xuehong Gao
Appl. Sci. 2023, 13(18), 10384; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810384 - 17 Sep 2023
Viewed by 948
Abstract
The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty [...] Read more.
The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty resulting in the deviation of risk values must also be considered in the risk assessment. In this regard, this study proposes a hybrid cloud model (CM)-enabled Dynamic Bayesian network (DBN) method to estimate the cable fire risk under uncertainty. In particular, the CM is initially applied to determine the membership degrees of the assessment data relative to different states of the root nodes; then, these degrees are considered the prior probabilities of DBN, where the dynamic risk profiles are reasoned. Subsequently, the Birnbaum and Fussell–Vesely importance measures are constructed to identify the key nodes for risk prevention and control, respectively. Moreover, a case study of the Chongqing Tobacco Logistics Distribution Center is conducted, the computational results of which indicate the proposed method’s decision-making effectiveness. Finally, a comparison of the reasoning results between the proposed and traditional methods is performed, presenting strong evidence that demonstrates the reliability of the proposed method. Full article
(This article belongs to the Special Issue Advances in Disaster Risk Sciences in the Era of Big Data)
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14 pages, 2822 KiB  
Article
Research on the Characteristics of Internet Public Opinion and Public Sentiment after the Sichuan Earthquake Based on the Perspective of Weibo
by Weiming He, Qinglu Yuan and Nan Li
Appl. Sci. 2023, 13(3), 1335; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031335 - 19 Jan 2023
Cited by 2 | Viewed by 1783
Abstract
In this paper, based on Sina Weibo data, a natural language processing (NLP) analysis method was used to analyze the temporal and spatial sequence characteristics of people’s attention and the characteristics of text content with the help of microblogs posted by people within [...] Read more.
In this paper, based on Sina Weibo data, a natural language processing (NLP) analysis method was used to analyze the temporal and spatial sequence characteristics of people’s attention and the characteristics of text content with the help of microblogs posted by people within 6 days after the 2022 Lushan M6.1, Maerkang M5.8 and Luding M6.8 earthquakes. Moreover, the same analysis method was used on the content of comments on microblogs posted by official media outlets within 6 days after the earthquakes to analyze the changes in people’s sentiments and the differences in the sentiments in various regions, and the influencing factors were also analyzed. The results of this research show the following: In terms of the spatial and temporal distributions, people’s attention was affected by the earthquakes themselves and their social impacts, and the first 2 h was often a period of an outbreak of attention, with the publishing areas mainly concentrated in Sichuan and Guangdong. In terms of people’s sentiments, the overall microblogging sentiment of the three earthquakes was positive, and the sentiment value of the people in Sichuan was generally low compared with that of the people in the other regions. Not only was the fluctuation in sentiment affected by the influence of the region, but it was also positively related to the sentiment of official microblogs. The results of this research provide reference for guiding people’s sentiments after earthquakes in the new media era. Full article
(This article belongs to the Special Issue Advances in Disaster Risk Sciences in the Era of Big Data)
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17 pages, 5793 KiB  
Article
Causality Analysis and Risk Assessment of Haze Disaster in Beijing
by Xiaobin Zhang and Bo Yu
Appl. Sci. 2022, 12(18), 9291; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189291 - 16 Sep 2022
Cited by 2 | Viewed by 1305
Abstract
Due to the lack of training data and effective haze disaster prediction model, the research on causality analysis and the risk prediction of haze disaster is mainly qualitative. In order to solve this problem, a nonlinear dynamic prediction model of Beijing haze disaster [...] Read more.
Due to the lack of training data and effective haze disaster prediction model, the research on causality analysis and the risk prediction of haze disaster is mainly qualitative. In order to solve this problem, a nonlinear dynamic prediction model of Beijing haze disaster was built in this study. Based on the macroscopic evaluation of multiple influencing factors of haze disaster in Beijing, a causality model and flow diagrams of the Beijing crude oil consumption system, Beijing coal consumption system, Beijing urban greening system and sulfur dioxide emission system in Hebei and Tianjin were established. The risk prediction of Beijing haze disaster was simulated at different conditions of air pollutant discharge level for the Beijing–Tianjin–Hebei region. Compared with the governance strategies of vehicle emission reduction, petrochemical production emission reduction, coal combustion emission reduction, greening and reducing dust and collaborative governance policy, the Beijing–Tianjin–Hebei cross-regional collaborative governance policy was more effective in controlling the haze disaster of Beijing. In the prediction, from 2011 to 2017, the air quality of Beijing changed from light pollution to good. By 2017, the PM2.5 of Beijing reduced to 75 µg/m3. From 2017 to 2035, the control effect of urban haze disaster for Beijing further strengthened. By 2035, the PM2.5 of Beijing reduced to 35 μg/m3. Finally, the PM2.5 of Beijing continued to reduce from 2035 to 2050. The speed of reduction for PM2.5 in Beijing slowed down. Meanwhile, the achievements of haze control in Beijing were consolidated. By 2050, the risk of haze disaster for Beijing was basically solved. The nonlinear dynamic prediction model in this study provides better promise toward the future control and prediction of global haze disaster under the condition of limited data. Full article
(This article belongs to the Special Issue Advances in Disaster Risk Sciences in the Era of Big Data)
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