气候变化领域集成服务门户(CCPortal): Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt

CCPortal

DOI	10.1007/s11069-020-04296-y
	Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt
	El-Haddad B.A.; Youssef A.M.; Pourghasemi H.R.; Pradhan B.; El-Shater A.-H.; El-Khashab M.H.
发表日期	2021
ISSN	0921030X
起始页码	83
结束页码	114
卷号	105 期号:1
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.
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
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206208
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
推荐引用方式 GB/T 7714	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],2021,105(1).
APA	El-Haddad B.A.,Youssef A.M.,Pourghasemi H.R.,Pradhan B.,El-Shater A.-H.,&El-Khashab M.H..(2021).Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt.Natural Hazards,105(1).
MLA	El-Haddad B.A.,et al."Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt".Natural Hazards 105.1(2021).