干旱区生态环境知识资源中心(Arid): Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms

Arid

DOI	10.1016/j.jhydrol.2023.129599
	Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms
	Guo, Xu; Gui, Xiaofan; Xiong, Hanxiang; Hu, Xiaojing; Li, Yonggang; Cui, Hao; Qiu, Yang; Ma, Chuanming
通讯作者	Guo, X ; Ma, CM
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	621
英文摘要	Groundwater potential mapping (GPM) provides the valuable information on groundwater volume that can be withdrawn from the aquifer without affecting the environmental conditions. In this study, GPM was innovatively explored by including three typical climate factors - precipitation (PRE), evaporation (EVA), and ground surface temperature (GST) - and by taking into consideration a total of 23 conditional groundwater factors during the model construction. Three ensemble learning models are used for the modeling process: random forest (RF), XGBoost, and LightGBM. The study was conducted in the southern regions of Yinchuan Plain, which is known for its temperate continental climate with low precipitation and high evaporation. It was found that all six adopted models (i.e., RF-C, XGBoost-C, LightGBM-C, RF, XGBoost, LightGBM) made reasonable predictions of groundwater potential, with areas with high and very high potential concentrated in the southwest region, where the Yellow River enters the study area. The LightGBM-C model performs the best (OA: 0.769, F1 score: 0.667, AUC: 0.921), while the RF model performs the worst (OA: 0.654, F1 score: 0.4, AUC: 0.757). According to the LightGBM-C model, there are more productive wells in areas with high groundwater potential (22 wells, 0.0152 per km2), while fewer non-productive wells are found (3 wells, 0.0021 per km2). The performance of LightGBM-C and RF-C models has been notably enhanced by climate factors (AUC + 0.073, OA + 0.025, F1 score + 0.052 for LightGBM, and AUC + 0.073, OA + 0.051, F1 score + 0.149 for RF). The further cumulative importance results indicate that the three ensemble models which considered climate factors demonstrated a substantial sensitivity to PRE (27.78%), EVA (28.14%), and GST (23.34%). In arid and semi-arid regions, PRE and EVA are highly recommended factors for GPM, while DD (35.17%), EV (35.22%), NDVI (28.06%) and GWD (25.77%) are also confirmed to be important when climate factors are not taken into account.
英文关键词	Groundwater potential Climate factors LightGBM Ensemble learning Yinchuan Plain Arid region Hydroinformatics
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001006736400001
WOS关键词	FREQUENCY RATIO MODEL ; WEIGHTS-OF-EVIDENCE ; YINCHUAN PLAIN ; LOGISTIC-REGRESSION ; SHALLOW GROUNDWATER ; SPATIAL PREDICTION ; DEEP GROUNDWATER ; GIS ; MACHINE ; CLASSIFICATION
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/2XILL650/397400
推荐引用方式 GB/T 7714	Guo, Xu,Gui, Xiaofan,Xiong, Hanxiang,et al. Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms[J],2023,621.
APA	Guo, Xu.,Gui, Xiaofan.,Xiong, Hanxiang.,Hu, Xiaojing.,Li, Yonggang.,...&Ma, Chuanming.(2023).Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms.JOURNAL OF HYDROLOGY,621.
MLA	Guo, Xu,et al."Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms".JOURNAL OF HYDROLOGY 621(2023).