干旱区生态环境知识资源中心(Arid): Machine-Learning Methods for Water Table Depth Prediction in Seasonal Freezing-Thawing Areas

Arid

DOI	10.1111/gwat.12913
	Machine-Learning Methods for Water Table Depth Prediction in Seasonal Freezing-Thawing Areas
	Zhao, Tianxing; Zhu, Yan; Ye, Ming; Mao, Wei; Zhang, Xiaoping; Yang, Jinzhong; Wu, Jingwei
通讯作者	Zhu, Y
来源期刊	GROUNDWATER
ISSN	0017-467X
EISSN	1745-6584
出版年	2020
卷号	58 期号:3 页码:419-431
英文摘要	Long-term and accurate predictions of regional groundwater hydrology are important for maintaining environmental sustainability in arid agricultural areas that experience seasonal freezing and thawing where serious water-saving measurements are used. In this study, we firstly developed a machine-learning method by integrating a multivariate time series controlled auto-regressive method and the ridge regression method (CAR-RR) for water table depth modeling. We applied and evaluated this model in the Hetao Irrigation District, located in northwest China where the freezing-thawing period is 5 months long. To train and validate the model, we used monthly data of water diversion, precipitation, evaporation, and drainage from 1995 to 2013. The CAR-RR model yielded more accurate results than the support vector regression (SVR) and multiple linear regression (MLR) models did in the validation period. To extend the model applicability during freezing-thawing periods, we included additional temperature information. We compared results obtained using temperature only during the freezing-thawing period with results obtained without temperature, which showed that the input data of the temperature during the freezing-thawing period significantly improved the model accuracy. To resolve the problem of capturing the peaks and troughs of CAR-RR, we further developed an integrated CAR-SVR model to consider the nonlinearity. The optimal model (CAR-SVR) was then used to predict the water table depth under future water-saving measurements. It demonstrated that water diversion was the most important factor affecting the water table depth. A water table depth with less than 3.64 billion m(3) water diversion will result in risks of environment problems.
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000529319500011
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; SUPPORT VECTOR MACHINE ; TIME-SERIES MODELS ; GROUNDWATER LEVELS ; NUMERICAL-MODEL ; HYBRID MODELS ; LEVEL ; SIMULATION ; ANN ; FLUCTUATIONS
WOS类目	Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324376
作者单位	[Zhao, Tianxing; Zhu, Yan; Mao, Wei; Yang, Jinzhong; Wu, Jingwei] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn, 8 Donghu South Rd, Wuhan 430072, Hubei, Peoples R China; [Ye, Ming] Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL 32306 USA; [Zhang, Xiaoping] Wuhan Univ, Sch Math & Stat, 8 Donghu South Rd, Wuhan 430072, Hubei, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Tianxing,Zhu, Yan,Ye, Ming,et al. Machine-Learning Methods for Water Table Depth Prediction in Seasonal Freezing-Thawing Areas[J],2020,58(3):419-431.
APA	Zhao, Tianxing.,Zhu, Yan.,Ye, Ming.,Mao, Wei.,Zhang, Xiaoping.,...&Wu, Jingwei.(2020).Machine-Learning Methods for Water Table Depth Prediction in Seasonal Freezing-Thawing Areas.GROUNDWATER,58(3),419-431.
MLA	Zhao, Tianxing,et al."Machine-Learning Methods for Water Table Depth Prediction in Seasonal Freezing-Thawing Areas".GROUNDWATER 58.3(2020):419-431.