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Applied Sciences
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Novel Research on Natural Disaster Prediction and Prevention Technology
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Novel Research on Natural Disaster Prediction and Prevention Technology

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 1820

Special Issue Editors

Dr. Ming Fu

E-Mail Website
Guest Editor
Hefei Institute for Public Safety Research, Tsinghua University, Hefei, China
Interests: safety monitoring; urban disaster evolution and assessment; natural disaster prediction; resilient urban infrastructure
Dr. Lida Huang

E-Mail Website
Guest Editor
Institute of Safety Science and Technology, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
Interests: urban disaster prevention; disaster monitoring and early warning

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue of our journal on “Novel Research on Natural Disaster Prediction and Prevention Technology”. With the increasing frequency and intensity of natural disasters, advancements in this field are crucial. Recent studies have focused on integrating meteorological and geological data to enhance forecast accuracy, and innovations in remote sensing, machine learning, and big data analytics are being harnessed to predict disasters such as rainstorms, floods, earthquakes, hurricanes, and so forth. Moreover, research on resilient infrastructure and community preparedness is gaining momentum.

This Special Issue highlights these developments, showcasing the latest scientific and technological advancements in natural disaster prediction and prevention. Submissions should provide insights into the integration of technology into emergency response, risk assessment methodologies, and the role of public policy in enhancing disaster resilience. We welcome interdisciplinary contributions that bring together perspectives from the environmental sciences, engineering, social sciences and information technology. We encourage authors to present empirical research, case studies, and theoretical explorations that demonstrate the effectiveness of novel approaches in mitigating the impact of disasters.

Please submit your manuscript by 20 May 2025 through our submission system. All submitted papers will undergo a rigorous peer-review process. Selected papers will be published in this Special Issue, providing a platform for researchers to share their findings with a global audience.

Dr. Ming Fu
Dr. Lida Huang
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.

Keywords

  • natural disaster prediction
  • disaster prevention technology
  • big data analytics
  • machine learning
  • remote sensing
  • risk assessment
  • resilient infrastructure
  • community resilience
  • emergency response
  • disaster forecasting

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Published Papers (2 papers)

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23 pages, 5397 KiB  
Article
A Systematic Analysis of Influencing Factors on Wind Resilience in a Coastal Historical District of China
by Bo Huang, Zhenmin Ou, Gang Zhao, Junwu Wang, Lanjun Liu, Sijun Lv, Bin Huang and Xueqi Liu
Appl. Sci. 2025, 15(14), 8116; https://doi.org/10.3390/app15148116 - 21 Jul 2025
Viewed by 323
Abstract
Historical districts are the mark of the continuity of urban history and are non-renewable. Typhoon disasters rank among the most serious and frequent natural threats to China’s coastal regions. Improving the wind resilience of China’s coastal historical districts is of great significance for [...] Read more.
Historical districts are the mark of the continuity of urban history and are non-renewable. Typhoon disasters rank among the most serious and frequent natural threats to China’s coastal regions. Improving the wind resilience of China’s coastal historical districts is of great significance for their protection and inheritance. Accurately analyzing the different characteristics of the influencing factors of wind resilience in China’s coastal historical districts can provide a theoretical basis for alleviating the damage caused by typhoons and formulating disaster prevention measures. This paper accurately identifies the main influencing factors of wind resilience in China’s coastal historical districts and constructs an influencing factor system from four aspects: block level, building level, typhoon characteristics, and emergency management. An IIM model for the systematic analysis of influencing factors of wind resilience in China’s coastal historical districts based on the Improved Decision Making Trial and Evaluation Laboratory (IDEMATEL), Interpretive Structural Modeling (ISM), and Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) methods is established. This allows us to explore the mechanism of action of internal influencing factors of typhoon disasters and construct an influencing factor system, in order to propose prevention measures from the perspective of typhoon disaster characteristics and the overall perspective of China’s coastal historical districts. The results show that the driving force of a building’s windproof design in China’s coastal historical districts is low, but its dependence is strong; the driving forces of block morphology, typhoon level, and emergency plan are strong, but their dependence is low. A building’s windproof design is a direct influencing factor of the wind resilience of China’s coastal historical districts; block morphology, typhoon level, and emergency plan are the most fundamental and key influencing factors of the wind resilience of China’s coastal historical districts. Full article
(This article belongs to the Special Issue Novel Research on Natural Disaster Prediction and Prevention Technology)
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25 pages, 18948 KiB  
Article
Rain-Induced Shallow Landslide Susceptibility Under Multiple Scenarios Based on Effective Antecedent Precipitation
by Chuanmei Cheng, Ying Li, Dong Zhu, Yu Liu, Yongqiu Wu, Degen Lin and Hao Guo
Appl. Sci. 2025, 15(11), 6241; https://doi.org/10.3390/app15116241 - 1 Jun 2025
Viewed by 791
Abstract
Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent [...] Read more.
Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. The optimal model is selected to simulate landslide susceptibility maps under four return periods (5, 10, 20, and 50 years). Additionally, the mean decreases in the Gini and SHAP values are employed to identify the most significant factors contributing to landslides. The results indicate the following: (1) Effective antecedent precipitation is the most influential factor in landslide occurrence, ranging from one to two times higher than other factors. (2) Most meteorological stations in the study area show antecedent effective precipitation that follows a lognormal distribution, mainly in coastal areas, with a secondary fit to the general extreme value distribution. The spatial distribution of antecedent effective precipitation is more prominent in the coastal and western mountainous regions, with lower values that then increase with longer return periods in central areas. (3) The XGBoost model achieves the best performance, with an area under the curve of 0.96 and an accuracy of 89.02%. (4) The landslide susceptibility maps for the four return periods reveal three high-risk zones: the southern coastal mountains, the western Zhejiang mountains, and the areas surrounding the hilly region of Shaoxing to Taizhou in central Zhejiang. This study provides dynamic decision-making support for the prevention and control of rainstorm-induced landslide risks. Full article
(This article belongs to the Special Issue Novel Research on Natural Disaster Prediction and Prevention Technology)
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