DOI: 10.1007/s11069-020-04296-y
论文题名: Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt
作者: El-Haddad B.A. ; Youssef A.M. ; Pourghasemi H.R. ; Pradhan B. ; El-Shater A.-H. ; El-Khashab M.H.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 105, 期: 1 起始页码: 83
结束页码: 114
语种: 英语
中文关键词: Data mining
; Egypt
; Floods
; GIS
; Modeling
; Remote sensing
; Susceptibility
英文关键词: ArcGIS
; comparative study
; data interpretation
; data mining
; flood forecasting
; hazard management
; machine learning
; mapping method
; performance assessment
; satellite imagery
; Egypt
英文摘要: Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in many countries require modeling of flood susceptibility to help in reducing the damages and impact of floods. The objective of the current work is to employ four data mining/machine learning models to generate flood susceptibility maps, namely boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA). This study was done in Wadi Qena Basin in Egypt. Flood inundated locations were determined and extracted from the interpretation of different datasets, including high-resolution satellite images (sentinel-2 and Astro digital) (after flood events), historical records, and intensive field works. In total, 342 flood inundated locations were mapped using ArcGIS 10.5, which separated into two groups; training (has 239 flood locations represents 70%) and validating (has 103 flood locations represents 30%), respectively. Nine themes of flood-influencing factors were prepared, including slope-angle, slope length, altitude, distance from main wadis, landuse/landcover, lithological units, curvature, slope-aspect, and topographic wetness index. The relationships between the flood-influencing factors and the flood inventory map were evaluated using the mentioned models (BRT, FDA, GLM, and MDA). The results were compared with flood inundating locations (validating flood sites), which were not used in constructing the models. The accuracy of the models was calculated through the success (training data) and prediction (validation data) rate curves according to the receiver operating characteristics (ROC) and the area under the curve (AUC). The results showed that the AUC for success and prediction rates are 0.783, 0.958, 0.816, 0.821 and 0.812, 0.856, 0.862, 0.769 for BRT, FDA, GLM, and MDA models, respectively. Subsequently, flood susceptibility maps were divided into five classes, including very low, low, moderate, high, and very high susceptibility. The results revealed that the BRT, FDA, GLM, and MDA models provide reasonable accuracy in flood susceptibility mapping. The produced susceptibility maps might be vitally important for future development activities in the area, especially in choosing new urban areas, infrastructural activities, and flood mitigation areas. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169007
Appears in Collections: 气候变化与战略
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作者单位: Faculty of Science, Geology Department, Sohag University, Sohag, 82524, Egypt; Applied Geology Sector, Geological Hazards Department, Saudi Geological Survey, Jeddah, 21514, Saudi Arabia; Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran; Faculty of Engineering and IT, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology Sydney, Sydney, NSW 2007, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, South Korea
Recommended Citation:
El-Haddad B.A.,Youssef A.M.,Pourghasemi H.R.,et al. Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt[J]. Natural Hazards,2021-01-01,105(1)