| Issue |
E3S Web of Conf.
Volume 406, 2023
2023 9th International Conference on Energy Materials and Environment Engineering (ICEMEE 2023)
|
|
|---|---|---|
| Article Number | 04008 | |
| Number of page(s) | 6 | |
| Section | Geographic Remote Sensing Application and Environmental Modeling | |
| DOI | https://doi.org/10.1051/e3sconf/202340604008 | |
| Published online | 31 July 2023 | |
Risk Assessment of Flood Disaster in Sichuan Province Based on GIS
1 China University of Geosciences Beijing Beijing, China
2 Chengdu University of Technology Chengdu, China
3 Southwest JiaoTong University Chengdu, China
a e-mail: 15611606571@163.com
b e-mail: a2966433530@163.com
c e-mail: 2020114035@my.swjtu.edu.cn
In the context of global climate change, flood disaster has become one of the significant disasters endangering human life. Sichuan Province has more annual precipitation and complex landforms, which suffer from yearly floods. To reveal the spatial distribution and spatiotemporal dynamic change characteristics, we developed a risk assessment model based on geographic information systems and natural disaster risk assessment theory. Considering both natural and human factors, precipitation, terrain, climate type, population, and GDP are selected as evaluation indexes. We Used the expert score-AHP method to obtain the annual flood disaster risk regionalization from 2017 to 2021. In the end, the results were compared with the entropy weight method in the objective weighting method. The results showed that the comprehensive flood disaster risk in Sichuan Province is higher in the east and lower in the west. The flood disaster risk in the eastern hill area, Panxi area, and northeast Sichuan Basin is generally higher. In contrast, that in the west Sichuan Plateau is lower. Based on the evaluation results, it is found that the risk assessment of flood disasters based on expert scoring-AHP method is more in line with the actual situation.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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