Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder

doi:10.3390/electronics12183839

	Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder
	Chen, Yanping 2; Wang, Yilun 2; Wu, Zhize 2; Zou, Le 2; Li, Wenbo 1
刊名	ELECTRONICS
	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.
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关键词	REPRESENTATIONS
WOS研究方向	Computer Science ; Engineering ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:001073625300001
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
内容类型	期刊论文
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).