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Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

文献类型：期刊论文

作者Liu, Jun4; Wang, Jiyan4; Xiong, Junnan3,4; Cheng, Weiming3; Sun, Huaizhang2; Yong, Zhiwei1; Wang, Nan5
刊名REMOTE SENSING
出版日期2021-12-01
卷号13期号:23页码:26
关键词flood susceptibility bivariate statistics models machine learning models hybrid models
DOI10.3390/rs13234945
通讯作者Xiong, Junnan(xiongjn@swpu.edu.cn)
英文摘要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.
WOS关键词SUPPORT VECTOR MACHINE ; LANDSLIDE SUSCEPTIBILITY ; ARTIFICIAL-INTELLIGENCE ; RANDOM FOREST ; RIVER-BASIN ; GIS ; SCALE ; CLASSIFICATION ; ALGORITHMS ; PREDICTION
资助项目Key R & D project of Sichuan Science and Technology Department[21QYCX0016] ; Key R & D project of Sichuan Science and Technology Department[2021YFQ0042] ; National Key R&D Program of China[2020YFD1100701] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20030302] ; Science and Technology Project of Xizang Autonomous Region[XZ201901-GA-07] ; National Flash Flood Investigation and Evaluation Project[SHZH-IWHR-57] ; Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture[Y99M4600AL]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000735127800001
出版者MDPI
资助机构Key R & D project of Sichuan Science and Technology Department ; National Key R&D Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Science and Technology Project of Xizang Autonomous Region ; National Flash Flood Investigation and Evaluation Project ; Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture
源URL[http://ir.igsnrr.ac.cn/handle/311030/169126]  
专题中国科学院地理科学与资源研究所
通讯作者Xiong, Junnan
作者单位1.Southwest Petr Univ, Sch Geosci & Technol, Chengdu 610500, Peoples R China
2.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Southwest Petr Univ, Sch Civil Engn & Geomat, Chengdu 610500, Peoples R China
5.Northeast Normal Univ, Sch Geog Sci, Changchun 130024, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Jun,Wang, Jiyan,Xiong, Junnan,et al. Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets[J]. REMOTE SENSING,2021,13(23):26.
APA Liu, Jun.,Wang, Jiyan.,Xiong, Junnan.,Cheng, Weiming.,Sun, Huaizhang.,...&Wang, Nan.(2021).Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets.REMOTE SENSING,13(23),26.
MLA Liu, Jun,et al."Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets".REMOTE SENSING 13.23(2021):26.

入库方式： OAI收割

来源：地理科学与资源研究所

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