Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area
Abstract This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81583-9 |
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| author | Erzong Zheng Yichen Zhang Jiquan Zhang Jiale Zhu Jiahao Yan Gexu Liu |
| author_facet | Erzong Zheng Yichen Zhang Jiquan Zhang Jiale Zhu Jiahao Yan Gexu Liu |
| author_sort | Erzong Zheng |
| collection | DOAJ |
| description | Abstract This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity. Coupled with the Graph Convolutional Network (GCN) model, the study calculates the emergency evacuation time for each raster point, providing a comprehensive assessment of the region’s evacuation capacity. Based on the evaluation results, the study proposes targeted improvement measures for areas identified as having poor emergency evacuation capacity, taking into account the existing infrastructure of the scenic area. By constructing an indicator system encompassing effectiveness, accessibility, and safety, the feasibility of each proposed enhancement strategy is assessed scientifically and rationally. Through these integrated tools and methodologies, this research significantly improves the accuracy of data processing, evaluation, and decision support, showcasing a comprehensive approach to scenic area research that provides critical support for geological disaster management, emergency planning, and the overall safety of the Changbai Mountain scenic area. |
| format | Article |
| id | doaj-art-9134233e12ef453d90deeb6f99adc4d7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9134233e12ef453d90deeb6f99adc4d72024-12-29T12:25:08ZengNature PortfolioScientific Reports2045-23222024-12-0114112510.1038/s41598-024-81583-9Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic AreaErzong Zheng0Yichen Zhang1Jiquan Zhang2Jiale Zhu3Jiahao Yan4Gexu Liu5College of Jilin Emergency Management, Changchun Institute of TechnologyCollege of Jilin Emergency Management, Changchun Institute of TechnologyInstitute of Natural Disaster Research, School of Environment, Northeast Normal UniversityCollege of Jilin Emergency Management, Changchun Institute of TechnologyCollege of Jilin Emergency Management, Changchun Institute of TechnologyCollege of Jilin Emergency Management, Changchun Institute of TechnologyAbstract This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity. Coupled with the Graph Convolutional Network (GCN) model, the study calculates the emergency evacuation time for each raster point, providing a comprehensive assessment of the region’s evacuation capacity. Based on the evaluation results, the study proposes targeted improvement measures for areas identified as having poor emergency evacuation capacity, taking into account the existing infrastructure of the scenic area. By constructing an indicator system encompassing effectiveness, accessibility, and safety, the feasibility of each proposed enhancement strategy is assessed scientifically and rationally. Through these integrated tools and methodologies, this research significantly improves the accuracy of data processing, evaluation, and decision support, showcasing a comprehensive approach to scenic area research that provides critical support for geological disaster management, emergency planning, and the overall safety of the Changbai Mountain scenic area.https://doi.org/10.1038/s41598-024-81583-9Emergency evacuationEnhancement strategiesGeological hazardsCollapseDebris flowReal-ESRGAN |
| spellingShingle | Erzong Zheng Yichen Zhang Jiquan Zhang Jiale Zhu Jiahao Yan Gexu Liu Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area Scientific Reports Emergency evacuation Enhancement strategies Geological hazards Collapse Debris flow Real-ESRGAN |
| title | Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area |
| title_full | Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area |
| title_fullStr | Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area |
| title_full_unstemmed | Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area |
| title_short | Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area |
| title_sort | deep learning based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in changbai mountain north scenic area |
| topic | Emergency evacuation Enhancement strategies Geological hazards Collapse Debris flow Real-ESRGAN |
| url | https://doi.org/10.1038/s41598-024-81583-9 |
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