干旱区生态环境知识资源中心(Arid): Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin

Arid

DOI	10.1016/j.ecolind.2022.109256
	Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin
	Wang, Zitao; Wang, Jianping; Han, Jinjun
通讯作者	Wang, JP
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2022
卷号	142
英文摘要	Substantial mineral resources are enriched in the arid endorheic basins; however, due to environmental constraints, these areas face water shortages as well as its extremely uneven spatiotemporal distribution, which restricts the development of local industry and agriculture. Identifying these areas of the high groundwater potential are useful for groundwater supply and its sustainable planning. In this study, the Qaidam Basin in Northwest China was taken as an example. We collected 17 conditioning factors (i.e., precipitation, evaporation, geology, soil, Topographic Wetness Index, Fractional Vegetation Cover, distance to rivers, river density, distance to roads, road density, distance to faults, fault density, slope, curvature, residential density, landcover, and geomorphology) affecting groundwater resources in arid areas. We also collected 139 groundwater samples and used random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) (associated with one-hot encoding) to predict the groundwater potential in this area. The Qaidam Basin was discretized into 420,000 sample points calculated in turn by the above three models. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to test the accuracy of the three methods. Results indicated that the prediction scores for the three methods were 0.742, 0.790, and 0.817, and the AUC was 0.783, 0.811, and 0.846, respectively. The result provided by CNN was more precise than the results provided by RF and DNN. Additionally, this study aims to investigate the effects of the aforementioned factors on groundwater potential. A total of 17 factors were combined with the Geodetector model to quantify their impacts and interactions on the groundwater potential of the Qaidam Basin. Results revealed that the critical factors affecting groundwater potential in the Qaidam Basin were geomorphology (0.183) and evaporation (0.144), and their combined contribution was 0.457. The influence of arbitrary two-factors on groundwater potential is larger than that of themselves, demonstrating linear or nonlinear enhancement between them and confirming that the factor selections were sensible. The method based on CNN-Geodetector provides a novel approach for calculating groundwater potential, selecting appropriate evaluation indicators and quantifying the driving factors in the arid endorheic basins.
英文关键词	Groundwater potential mapping (GPM) Driving factors Geographical detector Deep learning Qaidam Basin
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000872003000001
WOS关键词	NEURAL-NETWORK ; RANDOM FOREST ; LEVEL ; FLUCTUATIONS ; EVOLUTION ; SYSTEM ; MODEL
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392332
推荐引用方式 GB/T 7714	Wang, Zitao,Wang, Jianping,Han, Jinjun. Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin[J],2022,142.
APA	Wang, Zitao,Wang, Jianping,&Han, Jinjun.(2022).Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin.ECOLOGICAL INDICATORS,142.
MLA	Wang, Zitao,et al."Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin".ECOLOGICAL INDICATORS 142(2022).