{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:58:19Z","timestamp":1777575499670,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T00:00:00Z","timestamp":1547683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the projects of Application of remote sensing on water and soil conservation in Beijing and its demonstration","award":["Z161100001116102"],"award-info":[{"award-number":["Z161100001116102"]}]},{"name":"Key technology on dynamic warning of flash flood in Henan Province (China) and its application","award":["HNSW-SHZH-2015-06"],"award-info":[{"award-number":["HNSW-SHZH-2015-06"]}]},{"name":"Study on infiltration mechanisms of special underlying surface in coalmine goaf in Shanxi Province (China) and application of runoff generation and concentration theory","award":["ZNGZ2015-008_2"],"award-info":[{"award-number":["ZNGZ2015-008_2"]}]},{"name":"Research on spatio-temporal variable source runoff model and its mechanism","award":["JZ0145B2017"],"award-info":[{"award-number":["JZ0145B2017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number &gt; Digital elevation &gt; Slope &gt; River density &gt; Flash Flood preventions &gt; Topographic Wetness Index &gt; annual maximum 24 h precipitation &gt; annual maximum 3 h precipitation.<\/jats:p>","DOI":"10.3390\/rs11020170","type":"journal-article","created":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T11:30:27Z","timestamp":1547724627000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China"],"prefix":"10.3390","volume":"11","author":[{"given":"Meihong","family":"Ma","sequence":"first","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"},{"name":"College of water sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Changjun","family":"Liu","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0278-502X","authenticated-orcid":false,"given":"Gang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3516-1210","authenticated-orcid":false,"given":"Hongjie","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Geological Sciences University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"given":"Pengfei","family":"Jia","sequence":"additional","affiliation":[{"name":"CITIC Construction Co., Ltd., Beijing 100027, China"}]},{"given":"Dacheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Lab of Spatial Information Integration, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Huixiao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of water sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Yang","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,17]]},"reference":[{"key":"ref_1","unstructured":"Baker, V.R., Kochel, R.C., and Patton, P.C. 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