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GIS-Based Prediction and Prevention of Geological Disaster
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GIS-Based Prediction and Prevention of Geological Disaster

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Earth Science and Medical Geology".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 3452

Special Issue Editors

Prof. Dr. Renmao Yuan

E-Mail Website
Guest Editor
1. Institute of Geology, China Earthquake Administration, Beijing 100029, China
2. Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China
Interests: geological hazards; engineering geology; active fault; geohazard risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Qiqi Zhu

E-Mail Website
Guest Editor
School of Geography and Information Engineering, Future City Campus, China University of Geosciences, Wuhan 430079, China
Interests: high-resolution remote sensing; deep learning; land-use/land-cover classification; change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Yaohui Liu

E-Mail Website
Guest Editor
1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: remote sensing; GIS; deep learning; geohazards; vulnerability; risk management

Special Issue Information

Dear Colleagues,

Geological disasters have been one of the main natural disasters that plague the development of human society, economy, and culture, often causing a huge loss of life and property. Especially the regional distributed geohazards induced by earthquakes or storms, such as liquefaction, ground ruptures, landslides, and debris are potentially the most destructive among all geohazards. Therefore, it is of great significance for disaster prediction and prevention to use new theories, methods, and technologies to study the laws, mechanisms, and risks of geological disasters.

In recent years, with the technology of GIS, remote sensing, multi-source data fusion, and so on is widely used in earth science research, numerous data can be easily collected for geological disaster research. At the same time, some advanced methods, such as machine learning, deep learning, and GIS spatial analysis, provide an important basis for the deep study of geological disasters.

This Special Issue focuses on the prediction and prevention of geological disasters based on GIS, remote sensing, machine learning, deep learning, or other state-of-the-art technologies. Manuscripts on reviews, theoretical research, case study and policy relevance with the aim to help predict and prevent geological disasters are welcome for submission. We especially encourage to use the freely available remote sensing images and GIS-based data, as well as open-source processing software, which can be used and analyzed by researchers from all over the world. Topics of interest include, but are not limited to: 

  • GIS-based system for geological disaster prediction and early warning;
  • Rapid geological disaster susceptibility mapping using GIS and remote sensing data/methods;
  • Geohazards damage evaluation, risk assessment, and management;
  • Dynamic analysis of geological disasters using GIS and remote sensing data/methods;
  • Landslide detection using deep learning methods;
  • Post-disaster recovery monitor;
  • Formation mechanism of geological disasters;
  • Strategies for geological disaster risk reduction.

Prof. Dr. Renmao Yuan
Dr. Qiqi Zhu
Dr. Yaohui 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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

  • geological disasters
  • landslides
  • earthquakes
  • GIS
  • remote sensing
  • machine learning
  • deep learning
  • risk management
  • prediction
  • prevention

Published Papers (2 papers)

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Research

18 pages, 3908 KiB  
Article
Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China
by Xueling Wu and Junyang Wang
Int. J. Environ. Res. Public Health 2023, 20(6), 4977; https://doi.org/10.3390/ijerph20064977 - 11 Mar 2023
Cited by 7 | Viewed by 1310
Abstract
Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide [...] Read more.
Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models. Full article
(This article belongs to the Special Issue GIS-Based Prediction and Prevention of Geological Disaster)
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19 pages, 64091 KiB  
Article
Ecological Risk Assessment of Geological Disasters Based on Probability-Loss Framework: A Case Study of Fujian, China
by Leli Zong, Ming Zhang, Zi Chen, Xiaonan Niu, Guoguang Chen, Jie Zhang, Mo Zhou and Hongying Liu
Int. J. Environ. Res. Public Health 2023, 20(5), 4428; https://doi.org/10.3390/ijerph20054428 - 01 Mar 2023
Cited by 1 | Viewed by 1546
Abstract
Geological disaster could pose a great threat to human development and ecosystem health. An ecological risk assessment of geological disasters is critical for ecosystem management and prevention of risks. Herein, based on the “probability-loss” theory, a framework integrating the hazard, vulnerability, and potential [...] Read more.
Geological disaster could pose a great threat to human development and ecosystem health. An ecological risk assessment of geological disasters is critical for ecosystem management and prevention of risks. Herein, based on the “probability-loss” theory, a framework integrating the hazard, vulnerability, and potential damage for assessing the ecological risk of geological disasters was proposed and applied to Fujian Province. In the process, a random forest (RF) model was implemented for hazard assessment by integrating multiple factors, and landscape indices were adopted to analyze vulnerability. Meanwhile, ecosystem services and spatial population data were used to characterize the potential damage. Furthermore, the factors and mechanisms that impact the hazard and influence risk were analyzed. The results demonstrate that (1) the regions exhibiting high and very high levels of geological hazard cover an area of 10.72% and 4.59%, respectively, and are predominantly concentrated in the northeast and inland regions, often distributed along river valleys. Normalized difference vegetation index (NDVI), precipitation, elevation, and slope are the most important factors for the hazard. (2) The high ecological risk of the study area shows local clustering and global dispersion. Additionally, human activities have a significant influence on ecological risk. (3) The assessment results based on the RF model have high reliability with a better performance compared with the information quantity model, especially when identifying high-level hazard areas. Our study will improve research on the ecological risk posed by geological disasters and provide effective information for ecological planning and disaster mitigation. Full article
(This article belongs to the Special Issue GIS-Based Prediction and Prevention of Geological Disaster)
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