干旱区生态环境知识资源中心(Arid): Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations

Arid

DOI	10.1016/j.jhydrol.2023.129399
	Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations
	Wei, Yang; Wang, Fei; Hong, Bo; Yang, Shengtian
通讯作者	Wang, F
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	620
英文摘要	Owing to the lack of hydrological monitoring stations and groundwater observations, particularly for natural habitats, the distribution of groundwater along the Mountainous (alluvial fan)-Oasis-Desert System (MODS) in the middle reaches of the Tarim River has not yet been systematically investigated. The performance of ensemble algorithms with multi-source/multi-scale (30 m, 90 m, 250 m, 500 m, and 1000 m) remote sensing data to predict the groundwater levels of the MODS of the Tarim Basin should be investigated. To improve the current knowledge on the spatial distribution of groundwater in this region, we conducted a study on the applicability of ensemble learning algorithms for groundwater assessments in typical regions of dryland, namely the Oasis-Taklimakan Desert (OTD), Nature land in Oasis system (NLOS), Irrigation area (IA), and Oasis system (OS). The results showed that the R-2 values of the groundwater level prediction accuracy for the four ecosystems were 0.92, 0.96, 0.86, and 0.50, respectively, and their corresponding optimal ensemble algorithms were AR_GRBFN, AR_RF, RotationForest_MLP, and AR_ GRBFN. The scales corresponding to the optimal prediction accuracy of OTD, NLOS, IA, and OS were 250 m, 30 m, 250 m, and 90 m, respectively, and the effect of the scale on their respective groundwater level prediction accuracies (maximum value compared to minimum value) were 11.48%, 21.63%, 26.73%, and 10.59%. Random Subspace, Rotation Forest, and Additive Regression improved the pre-diction accuracy of the base learning method by 76%, 72%, and 64%, respectively, followed by Bagging, whereas Dagging greatly increased prediction errors. Rotation Forest and Random Subspace showed stable performances and guaranteed relatively low prediction errors. Among all ensemble algorithms, Additive Regression helped the base learners obtain relatively optimal prediction accuracies with high probabilities. Except for the OS system, groundwater level could be predicted with much greater accuracy at depths of >8 m than at <8 m. The contribution analysis showed that topography and land-use patterns controlled the spatial distribution of groundwater across MODS. The ensemble learning algorithm showed good performance using multi-source and multi-scale data.
英文关键词	Groundwater level Tarim basin Ensemble algorithms Scale effect Remote sensing
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001065814900001
WOS关键词	RIVER-BASIN ; LAND-USE ; LOWER REACHES ; COVER CHANGE ; HEIHE RIVER ; ARID REGION ; XINJIANG ; CHINA ; VEGETATION ; IMPACTS
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/2XILL650/397397
推荐引用方式 GB/T 7714	Wei, Yang,Wang, Fei,Hong, Bo,et al. Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations[J],2023,620.
APA	Wei, Yang,Wang, Fei,Hong, Bo,&Yang, Shengtian.(2023).Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations.JOURNAL OF HYDROLOGY,620.
MLA	Wei, Yang,et al."Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations".JOURNAL OF HYDROLOGY 620(2023).