Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method
Global climate change and human activities aggravate the frequency of flood disasters. Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster envir...
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| Veröffentlicht in: | Environmental monitoring and assessment 2020-06, Vol.192 (6), p.387, Article 387 |
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| creator | Yang, Minzhi Zhong, Ping-an Li, Jieyu Liu, Weifeng Li, Yinghui Yan, Kun Yuan, Yinyang Gao, Yihui |
| description | Global climate change and human activities aggravate the frequency of flood disasters. Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster environment, the disaster-bearing body, and the disaster prevention and mitigation capability into consideration comprehensively. Eleven influencing indexes including annual maximum 3-day rainfall and rainfall in flood season are selected, and the virtual sown area of crops is innovated. Taking the Huaihe River Basin (HRB) as the research area, the flood risk prediction of the basin is explored by using the long short-term memory (LSTM). The results show that LSTM can be successfully applied to flood risk prediction. The short-term prediction results of the model are good, and the area where the risk is seriously underestimated (the high and very high risk are identified as the very low risk) accounts for only 0.98% of the total basin on average. The prediction results can be used as a reference for watershed management organizations, so as to guide future flood disaster prevention. |
| doi_str_mv | 10.1007/s10661-020-08351-w |
| format | Article |
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Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster environment, the disaster-bearing body, and the disaster prevention and mitigation capability into consideration comprehensively. Eleven influencing indexes including annual maximum 3-day rainfall and rainfall in flood season are selected, and the virtual sown area of crops is innovated. Taking the Huaihe River Basin (HRB) as the research area, the flood risk prediction of the basin is explored by using the long short-term memory (LSTM). The results show that LSTM can be successfully applied to flood risk prediction. The short-term prediction results of the model are good, and the area where the risk is seriously underestimated (the high and very high risk are identified as the very low risk) accounts for only 0.98% of the total basin on average. The prediction results can be used as a reference for watershed management organizations, so as to guide future flood disaster prevention.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-020-08351-w</identifier><identifier>PMID: 32436015</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Annual rainfall ; Atmospheric Protection/Air Quality Control/Air Pollution ; Climate and human activity ; Climate change ; Disasters ; Earth and Environmental Science ; Ecology ; Economics ; Ecotoxicology ; Emergency preparedness ; Environment ; Environmental Management ; Environmental monitoring ; Environmental risk ; Environmental science ; Flood control ; Flood disasters ; Flood frequency ; Flood management ; Flood mapping ; Flood predictions ; Flood risk ; Floods ; Global climate ; Long short-term memory ; Mitigation ; Monitoring/Environmental Analysis ; Predictions ; Prevention ; Rain ; Rainfall ; Risk ; River basins ; Rivers ; Watershed management ; Zonation</subject><ispartof>Environmental monitoring and assessment, 2020-06, Vol.192 (6), p.387, Article 387</ispartof><rights>Springer Nature Switzerland AG 2020</rights><rights>Springer Nature Switzerland AG 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-2bc05de2308186dbd135f9deb372931720b0450d9c3cdba565c6997846f7c93f3</citedby><cites>FETCH-LOGICAL-c375t-2bc05de2308186dbd135f9deb372931720b0450d9c3cdba565c6997846f7c93f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-020-08351-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-020-08351-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27928,27929,41492,42561,51323</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32436015$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Minzhi</creatorcontrib><creatorcontrib>Zhong, Ping-an</creatorcontrib><creatorcontrib>Li, Jieyu</creatorcontrib><creatorcontrib>Liu, Weifeng</creatorcontrib><creatorcontrib>Li, Yinghui</creatorcontrib><creatorcontrib>Yan, Kun</creatorcontrib><creatorcontrib>Yuan, Yinyang</creatorcontrib><creatorcontrib>Gao, Yihui</creatorcontrib><title>Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>Global climate change and human activities aggravate the frequency of flood disasters. Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster environment, the disaster-bearing body, and the disaster prevention and mitigation capability into consideration comprehensively. Eleven influencing indexes including annual maximum 3-day rainfall and rainfall in flood season are selected, and the virtual sown area of crops is innovated. Taking the Huaihe River Basin (HRB) as the research area, the flood risk prediction of the basin is explored by using the long short-term memory (LSTM). The results show that LSTM can be successfully applied to flood risk prediction. The short-term prediction results of the model are good, and the area where the risk is seriously underestimated (the high and very high risk are identified as the very low risk) accounts for only 0.98% of the total basin on average. The prediction results can be used as a reference for watershed management organizations, so as to guide future flood disaster prevention.</description><subject>Annual rainfall</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Climate and human activity</subject><subject>Climate change</subject><subject>Disasters</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Economics</subject><subject>Ecotoxicology</subject><subject>Emergency preparedness</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental monitoring</subject><subject>Environmental risk</subject><subject>Environmental science</subject><subject>Flood control</subject><subject>Flood disasters</subject><subject>Flood frequency</subject><subject>Flood management</subject><subject>Flood mapping</subject><subject>Flood predictions</subject><subject>Flood risk</subject><subject>Floods</subject><subject>Global climate</subject><subject>Long short-term memory</subject><subject>Mitigation</subject><subject>Monitoring/Environmental Analysis</subject><subject>Predictions</subject><subject>Prevention</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Risk</subject><subject>River basins</subject><subject>Rivers</subject><subject>Watershed 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on intelligent prediction and zonation of basin-scale flood risk based on LSTM method</title><author>Yang, Minzhi ; Zhong, Ping-an ; Li, Jieyu ; Liu, Weifeng ; Li, Yinghui ; Yan, Kun ; Yuan, Yinyang ; Gao, Yihui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-2bc05de2308186dbd135f9deb372931720b0450d9c3cdba565c6997846f7c93f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Annual rainfall</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Climate and human activity</topic><topic>Climate change</topic><topic>Disasters</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Economics</topic><topic>Ecotoxicology</topic><topic>Emergency preparedness</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Environmental monitoring</topic><topic>Environmental risk</topic><topic>Environmental 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Minzhi</au><au>Zhong, Ping-an</au><au>Li, Jieyu</au><au>Liu, Weifeng</au><au>Li, Yinghui</au><au>Yan, Kun</au><au>Yuan, Yinyang</au><au>Gao, Yihui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>192</volume><issue>6</issue><spage>387</spage><pages>387-</pages><artnum>387</artnum><issn>0167-6369</issn><eissn>1573-2959</eissn><abstract>Global climate change and human activities aggravate the frequency of flood disasters. Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster environment, the disaster-bearing body, and the disaster prevention and mitigation capability into consideration comprehensively. Eleven influencing indexes including annual maximum 3-day rainfall and rainfall in flood season are selected, and the virtual sown area of crops is innovated. Taking the Huaihe River Basin (HRB) as the research area, the flood risk prediction of the basin is explored by using the long short-term memory (LSTM). The results show that LSTM can be successfully applied to flood risk prediction. The short-term prediction results of the model are good, and the area where the risk is seriously underestimated (the high and very high risk are identified as the very low risk) accounts for only 0.98% of the total basin on average. The prediction results can be used as a reference for watershed management organizations, so as to guide future flood disaster prevention.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>32436015</pmid><doi>10.1007/s10661-020-08351-w</doi></addata></record> |
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| subjects | Annual rainfall Atmospheric Protection/Air Quality Control/Air Pollution Climate and human activity Climate change Disasters Earth and Environmental Science Ecology Economics Ecotoxicology Emergency preparedness Environment Environmental Management Environmental monitoring Environmental risk Environmental science Flood control Flood disasters Flood frequency Flood management Flood mapping Flood predictions Flood risk Floods Global climate Long short-term memory Mitigation Monitoring/Environmental Analysis Predictions Prevention Rain Rainfall Risk River basins Rivers Watershed management Zonation |
| title | Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method |
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