干旱区生态环境知识资源中心(Arid): Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia

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DOI	10.2166/nh.2023.083
	Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia
	Seifu, Tesema Kebede; Eshetu, Kidist Demessie; Woldesenbet, Tekalegn Ayele; Alemayehu, Taye; Ayenew, Tenalem
通讯作者	Seifu, TK
来源期刊	HYDROLOGY RESEARCH
ISSN	1998-9563
EISSN	2224-7955
出版年	2023
卷号	54 期号:10 页码:1246-1266
英文摘要	Groundwater availability is one of the key anxieties in most semi-arid regions of Ethiopia. The purpose of this study was to investigate the groundwater potential zone map of the alluvial plain of Gambela. The study applied analytic hierarchy process (AHP) models with four different machine learning algorithms: random forest classifier (RFC), gradient boosting classifier (GBC), decision tree classifier (DTC), and K-neighbor classifier (KNC). The features that are used as predictors include geology, geomorphology, slope, soil, lineament density, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic roughness index (TRI), and rainfall. The final output of the groundwater potential zone was classified as low, moderate, high, and very high potential zones. The authentication through receiver operating curve (ROC) shows 78.2, 93.4, 92.5, 72.4, and 87.7% values of area under the curve (AUC) for AHP, RFC, GBC, DTC, and KNC, respectively. The results show that RFC and GBC are the best GWPZ map estimator. The study also shows that rainfall and geomorphology are the primary factors influencing the GWPZ. The outcome might promote improved management alternatives in other areas of the country with a comparable climate.
英文关键词	Gambela Plain groundwater potential machine learning random forest remote sensing ROC
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001071194500001
WOS关键词	RANDOM FOREST MODELS ; GIS ; MULTIVARIATE ; RECHARGE
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396896
推荐引用方式 GB/T 7714	Seifu, Tesema Kebede,Eshetu, Kidist Demessie,Woldesenbet, Tekalegn Ayele,et al. Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia[J],2023,54(10):1246-1266.
APA	Seifu, Tesema Kebede,Eshetu, Kidist Demessie,Woldesenbet, Tekalegn Ayele,Alemayehu, Taye,&Ayenew, Tenalem.(2023).Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia.HYDROLOGY RESEARCH,54(10),1246-1266.
MLA	Seifu, Tesema Kebede,et al."Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia".HYDROLOGY RESEARCH 54.10(2023):1246-1266.