干旱区生态环境知识资源中心(Arid): Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors

Arid

DOI	10.1016/j.jhydrol.2020.125197
	Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors
	Naghibi, Seyed Amir; Hashemi, Hossein; Berndtsson, Ronny; Lee, Saro
通讯作者	Naghibi, SA
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2020
卷号	589
英文摘要	Groundwater (GW) resources provide a large share of the world's water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challenge to achieve a GW potential assessment. In this study, we developed methods to produce reliable GW potential maps (GWPM) with only digital elevation model (DEM)-derived data as inputs. To achieve this objective, a case study area in Iran was selected and 13 factors were extracted from the DEM. A spring location dataset was obtained from the water sector organizations and, along with the non-spring locations, fed into machine learning algorithms for training and validation. For delineating reliable GW potential, algorithms including random forest (RF) and its developed version, parallel RF (PRF), as well as extreme gradient boosting (XGB) with different boosters were used. The area under the receiver operating characteristics curve indicated that the PRF and XGB with linear booster give similar high accuracy (about 86%) for GWPM. The most important factors for accurate GWPM in the modeling procedure were convergence, topographic wetness index, river density, and altitude. Overall, we conclude that high-accuracy GWPMs can be produced with only DEM-derived factors with acceptable accuracy. The developed methodology can be employed to produce initial information for GW exploitation in areas facing a lack of data.
英文关键词	Groundwater potential Data scarcity Parallel random forest Extreme gradient boosting GIS
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000568830400060
WOS关键词	MODELING SPATIAL-PATTERNS ; SUPPORT VECTOR MACHINE ; FREQUENCY RATIO ; GIS ; RECHARGE ; WEIGHTS ; REGION ; RIVER
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326255
作者单位	[Naghibi, Seyed Amir; Hashemi, Hossein; Berndtsson, Ronny] Lund Univ, Dept Water Resources Engn, Lund, Sweden; [Naghibi, Seyed Amir; Hashemi, Hossein; Berndtsson, Ronny] Lund Univ, Ctr Middle Eastern Studies, Lund, Sweden; [Lee, Saro] Korea Inst Geosci & Mineral Resources KIGAM, Div Geosci Res Platform, 124 Gwahang No, Daejeon 34132, South Korea; [Lee, Saro] Korea Univ Sci & Technol, 217 Gajeong Ro, Daejeon 34113, South Korea
推荐引用方式 GB/T 7714	Naghibi, Seyed Amir,Hashemi, Hossein,Berndtsson, Ronny,et al. Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors[J],2020,589.
APA	Naghibi, Seyed Amir,Hashemi, Hossein,Berndtsson, Ronny,&Lee, Saro.(2020).Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors.JOURNAL OF HYDROLOGY,589.
MLA	Naghibi, Seyed Amir,et al."Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors".JOURNAL OF HYDROLOGY 589(2020).