Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder
文献类型：期刊论文
| 作者 | Chen, Yanping2; Wang, Yilun2; Wu, Zhize2; Zou, Le2; Li, Wenbo1
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| 刊名 | ELECTRONICS
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 12 |
| 关键词 | flood disaster stacked denoising autoencoders deep learning |
| DOI | 10.3390/electronics12183839 |
| 通讯作者 | Li, Wenbo(wbli@iim.ac.cn) |
| 英文摘要 | In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan Province, where a major flood disaster occurred in 2021, as an example to analyze the impact factors of urban flooding and conduct a risk assessment. Indicators were first selected from population, housing, and the economy, and correlation analysis was used to optimize the indicator system. Then, a deep clustering network model based on a stacked denoising autoencoder (SDAE) was constructed, the feature information implied in the disaster indicators was abstracted into potential features through the coding and decoding of the network, and a small number of potential features were used to express the complex relationship between the disaster indicators. The results of the study show that the high-risk areas of flood damage in Henan Province in 2021 account for 2.3%, the medium-risk areas account for 9.4%, and the low-risk areas account for 80.3%. These evaluation results are in line with the actual situation in Henan Province, and the division of the grade in some areas is more reasonable compared with the entropy weighting method, which is a commonly used method of disaster assessment. The new model does not need to calculate weights to cope with changes in indicators and disaster conditions. The research results can provide scientific reference for urban flood risk management, disaster prevention and mitigation, and regional planning. |
| WOS关键词 | REPRESENTATIONS |
| 资助项目 | Anhui Provincial Natural Science Foundation[2108085J19] ; National Nature Science Foundation of China[41871302] ; grant of Scientific Research and Talent Development Foundation of the Hefei University[21-22RC15] ; Program for Scientific Research Innovation Team in Colleges and Universities of Anhui Province[2022AH010095] ; Key Research Plan of Anhui[2022k07020011] ; Key Research Plan of Anhui[202104d07020006] ; open fund of Information Materials and Intelligent Sensing Laboratory ofAnhui Province[IMIS202205] ; AI General Computing Platform of Hefei University |
| WOS研究方向 | Computer Science ; Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001073625300001 |
| 出版者 | MDPI |
| 资助机构 | Anhui Provincial Natural Science Foundation ; National Nature Science Foundation of China ; grant of Scientific Research and Talent Development Foundation of the Hefei University ; Program for Scientific Research Innovation Team in Colleges and Universities of Anhui Province ; Key Research Plan of Anhui ; open fund of Information Materials and Intelligent Sensing Laboratory ofAnhui Province ; AI General Computing Platform of Hefei University |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/132537]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Li, Wenbo |
| 作者单位 | 1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Hefei Univ, Sch Artificial Intelligence & Big Data, Hefei 230601, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yanping,Wang, Yilun,Wu, Zhize,et al. Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder[J]. ELECTRONICS,2023,12. |
| APA | Chen, Yanping,Wang, Yilun,Wu, Zhize,Zou, Le,&Li, Wenbo.(2023).Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder.ELECTRONICS,12. |
| MLA | Chen, Yanping,et al."Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder".ELECTRONICS 12(2023). |
入库方式： OAI收割
来源：合肥物质科学研究院
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