干旱区生态环境知识资源中心(Arid): Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China

Arid

DOI	10.1016/j.agwat.2021.107032
	Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China
	Ao, Chang; Zeng, Wenzhi; Wu, Lifeng; Qian, Long; Srivastava, Amit Kumar; Gaiser, Thomas
通讯作者	Zeng, WZ (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China. ; Wu, LF (corresponding author), Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China.
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2021
卷号	255
英文摘要	A large amount of continuous input data is used to estimate groundwater level (GWL) by using machine learning models. However, data collection is very difficult and costly in undeveloped countries. Therefore, obtaining a general model and using less input data is the key to popularizing the application of machine learning models for estimating groundwater levels. This study evaluated the potential of the kernel-based nonlinear extension of the Arps decline model (KNEA), long short-term memory network (LSTM) and gated recurrent unit (GRU) for accurately estimating GWL in the Hetao Irrigation District in China. All models were developed using monthly records from 143 monitoring wells between 1990 and 2015. Eight input combinations (including the one-month prior GWL, air temperature, global solar radiation, precipitation and amount of irrigation) were applied to explore the possibility of improving model accuracy using less input data. In addition, the general performance of the models was evaluated by cross validation. The results showed that the KNEA model was superior to the LSTM and GRU models for all input combinations using the local application. For cross-district application, the average statistical results indicated that the LSTM (RMSE = 0.45 m and R2 = 0.78) and GRU (RMSE = 0.48 m and R2 = 0.76) models performed better than the KNEA model (RMSE = 0.70 m and R2 = 0.62), and the LSTM model achieved the highest accuracy and stability. For input data, these three models had difficulty obtaining satisfactory monthly GWLs using meteorological and irrigation data without pervious GWL data. Adding meteorological data on the basis of the historical GWL greatly improved the accuracy of the models. Compared with PREC and GSR, adding temperature input had the best improvement. However, adding large-scale average irrigation data did not significantly improve the accuracy of the models. In addition, the LSTM model and input data of the historical GWLs and temperature were recommended in arid and semiarid agricultural areas with limited data.
英文关键词	Machine learning models Groundwater LSTM KNEA GRU
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000679334400003
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; SUPPORT VECTOR MACHINES ; ARPS DECLINE MODEL ; SHALLOW GROUNDWATER ; LEVEL ; WATER ; SIMULATION ; PERFORMANCE ; REGRESSION ; SYSTEM
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368492
作者单位	[Ao, Chang; Zeng, Wenzhi] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Wu, Lifeng] Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China; [Qian, Long] Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Guangdong, Peoples R China; [Srivastava, Amit Kumar; Gaiser, Thomas] Univ Bonn, Inst Crop Sci & Resource Conservat INRES, Crop Sci Grp, Katzenburgweg 5, D-53115 Bonn, Germany
推荐引用方式 GB/T 7714	Ao, Chang,Zeng, Wenzhi,Wu, Lifeng,et al. Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China[J],2021,255.
APA	Ao, Chang,Zeng, Wenzhi,Wu, Lifeng,Qian, Long,Srivastava, Amit Kumar,&Gaiser, Thomas.(2021).Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China.AGRICULTURAL WATER MANAGEMENT,255.
MLA	Ao, Chang,et al."Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China".AGRICULTURAL WATER MANAGEMENT 255(2021).