{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T03:03:02Z","timestamp":1782183782357,"version":"3.54.5"},"reference-count":126,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region","award":["2018D04027"],"award-info":[{"award-number":["2018D04027"]}]},{"name":"Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region","award":["2020LCJ02"],"award-info":[{"award-number":["2020LCJ02"]}]},{"name":"Open Project of Key Laboratory of Xinjiang Uygur Autonomous Region","award":["2022B03001-3"],"award-info":[{"award-number":["2022B03001-3"]}]},{"name":"The 2020 Qinghai Kunlun talents-Leading scientists project","award":["2018D04027"],"award-info":[{"award-number":["2018D04027"]}]},{"name":"The 2020 Qinghai Kunlun talents-Leading scientists project","award":["2020LCJ02"],"award-info":[{"award-number":["2020LCJ02"]}]},{"name":"The 2020 Qinghai Kunlun talents-Leading scientists project","award":["2022B03001-3"],"award-info":[{"award-number":["2022B03001-3"]}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2018D04027"],"award-info":[{"award-number":["2018D04027"]}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2020LCJ02"],"award-info":[{"award-number":["2020LCJ02"]}]},{"name":"Key Research and Development Program of Xinjiang Uygur Autonomous Region","award":["2022B03001-3"],"award-info":[{"award-number":["2022B03001-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Modeling and assessing the susceptibility of snowmelt floods is critical for flood hazard management. However, the current research on snowmelt flood susceptibility lacks a valid large-scale modeling approach. In this study, a novel high-performance deep learning model called Swin Transformer was used to assess snowmelt susceptibility in the Kunlun Mountains region, where snowmelt floods occur frequently. Support vector machine (SVM), random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) were also involved in the performance comparison. Eighteen potential conditioning factors were combined with a historical flood inventory to form the database. Apart from the susceptibility assessment, sensitivity analysis was also conducted to reflect the impact of the conditioning factors on the susceptibility of different types of snowmelt floods. The results showed that Swin Transformer achieved the highest score in the model performance test (AUC = 0.99) and successfully identified the relationship between conditioning factors and snowmelt flooding. Elevation and distance to rivers are the most important factors that affect snowmelt flooding in the study region, whereas rainfall and snow water equivalent are the dominant natural factors for mixed and warming types. In addition, the north-central parts of the study area have high susceptibility to snowmelt flooding. The methods and results can provide scientific support for snowmelt flood modeling and disaster management.<\/jats:p>","DOI":"10.3390\/rs14246360","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T02:54:02Z","timestamp":1671159242000},"page":"6360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method"],"prefix":"10.3390","volume":"14","author":[{"given":"Ruibiao","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9126-8357","authenticated-orcid":false,"given":"Guoxiong","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-0912","authenticated-orcid":false,"given":"Wenqiang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anming","family":"Bao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China"},{"name":"China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad 45320, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","unstructured":"Pascaline, W., Debarati, G.-S., Denis, M., CRED, and UNISDR (2015). 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