干旱区生态环境知识资源中心(Arid): Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt

Arid

DOI	10.1007/s12517-021-06466-z
	Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt
	Abu El-Magd, Sherif Ahmed; Pradhan, Biswajeet; Alamri, Abdullah
通讯作者	Pradhan, B (corresponding author), Univ Technol Sydney, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Fac Engn & IT, Sydney, NSW 2007, Australia. ; Pradhan, B (corresponding author), Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Change, Bangi 43600, Selangor, Malaysia.
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2021
卷号	14 期号:4
英文摘要	In the work described here, flash flood prediction mapping for the Wadi El-Laqeita in the Central Eastern Desert of Egypt was established, using machine learning approaches involving two algorithms-extreme gradient boosting (XGBoost) and k-nearest neighbor (KNN). Flash flood driving factors, including elevation, slope, curvature, slope-aspect, lithological rock units, distance from streams, stream density, and topographic wetness index (TWI) were selected. Based on the machine learning models, the XGBoost and KNN algorithms were quite similar, in terms of variables importance, with distance from the stream network, slope angle, elevation, and stream density identified as the key driving factors, in order of importance. It is often difficult to allocate model parameter settings; therefore, hyper-parameter setting optimization was applied to improve model prediction performance. The models were trained using 70% flooding location and 70% non-flooding data, with the remaining 30% flooding and 30% non-flooding location data used for model and simulation result validation. The applied models exhibited accuracies of 90.2% and 80.7% for XGBoost and KNN, respectively, showing that the XGBoost algorithm performed better than KNN in this situation. Therefore, XGBoost was used in a powerful approach to flash flood prediction mapping, with the obtained predictions providing important guidance for decision-makers with respect to future study site development.
英文关键词	Flash floods Extreme gradient boosting K-nearest neighbor GIS Wadi El-Laqeita
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000620382400011
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
来源机构	King Saud University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349538
作者单位	[Abu El-Magd, Sherif Ahmed] Suez Univ, Dept Geol, Fac Sci, Suez, Egypt; [Pradhan, Biswajeet] Univ Technol Sydney, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Fac Engn & IT, Sydney, NSW 2007, Australia; [Pradhan, Biswajeet] Univ Kebangsaan Malaysia, Earth Observat Ctr, Inst Climate Change, Bangi 43600, Selangor, Malaysia; [Alamri, Abdullah] King Saud Univ, Coll Sci, Dept Geol & Geophys, POB 2455, Riyadh 11451, Saudi Arabia
推荐引用方式 GB/T 7714	Abu El-Magd, Sherif Ahmed,Pradhan, Biswajeet,Alamri, Abdullah. Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt[J]. King Saud University,2021,14(4).
APA	Abu El-Magd, Sherif Ahmed,Pradhan, Biswajeet,&Alamri, Abdullah.(2021).Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt.ARABIAN JOURNAL OF GEOSCIENCES,14(4).
MLA	Abu El-Magd, Sherif Ahmed,et al."Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt".ARABIAN JOURNAL OF GEOSCIENCES 14.4(2021).