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Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods

文献类型：期刊论文

作者Chen, Wei10,11,12; Li, Yang12; Xue, Weifeng9,12; Shahabi, Himan8; Li, Shaojun7; Hong, Haoyuan4,5,6; Wang, Xiaojing12; Bian, Huiyuan12; Zhang, Shuai12; Pradhan, Biswajeet2,3
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2020-01-20
卷号701页码:11
关键词Flood susceptibility assessment Naive Bayes tree Alternating decision tree Random forest
ISSN号0048-9697
DOI10.1016/j.scitotenv.2019.134979
英文摘要Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naive Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets. (C) 2019 Elsevier B.V. All rights reserved.
资助项目International Partnership Program of Chinese Academy of Sciences[115242KYSB20170022] ; National Natural Science Foundation of China[41807192] ; National Natural Science Foundation of China[U1765206] ; Natural Science Basic Research Program of Shaanxi[2019JLM-7] ; Natural Science Basic Research Program of Shaanxi[2019JQ-094] ; China Postdoctoral Science Foundation[2018T111084] ; China Postdoctoral Science Foundation[2017M613168] ; Shaanxi Province Postdoctoral Science Foundation[2017BSHYDZZ07] ; Iran National Science Foundation (INSF)[96004000]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000498801400042
出版者ELSEVIER
源URL[http://119.78.100.198/handle/2S6PX9GI/23151]  
专题中科院武汉岩土力学所
通讯作者Hong, Haoyuan
作者单位1.Univ Teknol Malaysia, Fac Built Environm & Surveying, Johor Baharu 81310, Malaysia
2.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea
3.Univ Technol Sydney, Fac Engn & IT, CAMGIS, Sydney, NSW 2007, Australia
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
5.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China
6.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China
7.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China
8.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran
9.Shaanxi Coal & Chem Technol Inst Co Ltd, Xian 710065, Shaanxi, Peoples R China
10.Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Wei,Li, Yang,Xue, Weifeng,et al. Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,701:11.
APA Chen, Wei.,Li, Yang.,Xue, Weifeng.,Shahabi, Himan.,Li, Shaojun.,...&Bin Ahmad, Baharin.(2020).Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods.SCIENCE OF THE TOTAL ENVIRONMENT,701,11.
MLA Chen, Wei,et al."Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods".SCIENCE OF THE TOTAL ENVIRONMENT 701(2020):11.

入库方式： OAI收割

来源：武汉岩土力学研究所

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