Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China

Haoyuan Hong; Paraskevas Tsangaratos; Ioanna Ilia; Junzhi Liu; A-Xing Zhu; Wei Chen

doi:10.1016/j.scitotenv.2017.12.256

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China

Sci Total Environ. 2018 Jun 1:625:575-588. doi: 10.1016/j.scitotenv.2017.12.256. Epub 2017 Dec 30.

Authors

Haoyuan Hong¹, Paraskevas Tsangaratos², Ioanna Ilia³, Junzhi Liu⁴, A-Xing Zhu¹, Wei Chen⁵

Affiliations

¹ Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China.
² National Technical University of Athens, School of Mining and Metallurgical Engineering, Department of Geological Sciences, Laboratory of Engineering Geology and Hydrogeology, Zografou Campus: Heroon Polytechniou 9, 15780 Zografou, Greece. Electronic address: ptsag@metal.ntua.gr.
³ National Technical University of Athens, School of Mining and Metallurgical Engineering, Department of Geological Sciences, Laboratory of Engineering Geology and Hydrogeology, Zografou Campus: Heroon Polytechniou 9, 15780 Zografou, Greece.
⁴ Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China. Electronic address: liujunzhi@njnu.edu.cn.
⁵ College of Geology and Environment, Xi'an University of Science and Technology, Xi'an 710054, China.

PMID: 29291572
DOI: 10.1016/j.scitotenv.2017.12.256

Abstract

In China, floods are considered as the most frequent natural disaster responsible for severe economic losses and serious damages recorded in agriculture and urban infrastructure. Based on the international experience prevention of flood events may not be completely possible, however identifying susceptible and vulnerable areas through prediction models is considered as a more visible task with flood susceptibility mapping being an essential tool for flood mitigation strategies and disaster preparedness. In this context, the present study proposes a novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods. The novelty of the presented approach is the usage of fuzzy-WofE that had a twofold purpose. Firstly, to create an initial flood susceptibility map in order to identify non-flood areas and secondly to weight the importance of flood related variables which influence flooding. Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM) were implemented considering eleven flood related variables, namely: lithology, soil cover, elevation, slope angle, aspect, topographic wetness index, stream power index, sediment transport index, plan curvature, profile curvature and distance from river network. The efficiency of this new approach was evaluated using area under curve (AUC) which measured the prediction and success rates. According to the outcomes of the performed analysis, the fuzzy WofE-SVM model was the model with the highest predictive performance (AUC value, 0.9865) which also appeared to be statistical significant different from the other predictive models, fuzzy WofE-RF (AUC value, 0.9756) and fuzzy WofE-LR (AUC value, 0.9652). The proposed methodology and the produced flood susceptibility map could assist researchers and local governments in flood mitigation strategies.

Keywords: China; Data mining methods; Flood susceptibility; Fuzzy WofE.