{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:29:32Z","timestamp":1783610972441,"version":"3.55.0"},"reference-count":82,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R &amp; D project of Sichuan Science and Technology Department","award":["Grant No. 21QYCX0016"],"award-info":[{"award-number":["Grant No. 21QYCX0016"]}]},{"name":"National Key R&amp;D Program of China","award":["2020YFD1100701"],"award-info":[{"award-number":["2020YFD1100701"]}]},{"name":"trategic Priority Research Program of the Chinese Academy of Sciences","award":["Grant No. XDA20030302"],"award-info":[{"award-number":["Grant No. XDA20030302"]}]},{"name":"Science and Technology Project of Xizang Autonomous Region","award":["Grant No. XZ201901-GA-07"],"award-info":[{"award-number":["Grant No. XZ201901-GA-07"]}]},{"name":"National Flash Flood Investigation and Evaluation Project","award":["Grant No. SHZH-IWHR-57"],"award-info":[{"award-number":["Grant No. SHZH-IWHR-57"]}]},{"name":"Project form Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefec-ture","award":["Grant No.Y99M4600AL"],"award-info":[{"award-number":["Grant No.Y99M4600AL"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.<\/jats:p>","DOI":"10.3390\/rs13234945","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4945","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets"],"prefix":"10.3390","volume":"13","author":[{"given":"Jun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junnan","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"},{"name":"Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1580-4979","authenticated-orcid":false,"given":"Weiming","family":"Cheng","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaizhang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Yong","sequence":"additional","affiliation":[{"name":"School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.5194\/nhess-19-19-2019","article-title":"Ensemble flood forecasting considering dominant runoff processes\u2014Part 1: Set-up and application to nested basins (Emme, Switzerland)","volume":"19","author":"Antonetti","year":"2019","journal-title":"Nat. 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