title: Groundwater level prediction using a SOM-aided stepwise cluster inference model.
abstract: Accurate groundwater level (GWL) prediction can contribute to sustaining reliable water supply to domestic, agricultural and industrial uses as well as ecological services, especially in arid and semi-arid areas. In this paper, a regional GWL modeling framework was first presented through coupling both spatial and temporal clustering techniques. Specifically, the self-organizing map (SOM) was applied to identify spatially homogeneous clusters of GWL piezometers, while GWL time series forecasting was performed through developing a stepwise cluster multisite inference model with various predictors including climate conditions, well extractions, surface runoffs, reservoir operations and GWL measurements at previous steps. The proposed modeling approach was then demonstrated by a case of an arid irrigation district in the western Hexi Corridor, northwest China. Spatial clustering analysis identified 6 regionally representative central piezometers out of 30, for which sensitivity and uncertainty analysis were carried out regarding GWL predictions. As the stepwise cluster tree provided uncertain predictions, we added an AR(1) error model to the mean prediction to forecast GWL 1 month ahead. Model performance indicators suggest that the modeling system is a useful tool to aid decision-making for informed groundwater resource management in arid areas, and would have a great potential to extend its applications to more areas or regions in the future.
authors: State Key Laboratory of Hydroscience and Engineering, Dept. of Hydraulic Engineering, Tsinghua University, Beijing 100084, China. Electronic address: hanjc2012@gmail.com.
authors: Jing-Cheng
authors: JC
authors: Han
authors: State Key Laboratory of Hydroscience and Engineering, Dept. of Hydraulic Engineering, Tsinghua University, Beijing 100084, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China. Electronic address: yuefeihuang@tsinghua.edu.cn.
authors: Yuefei
authors: Y
authors: Huang
authors: Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada.
authors: Zhong
authors: Z
authors: Li
authors: State Key Laboratory of Hydroscience and Engineering, Dept. of Hydraulic Engineering, Tsinghua University, Beijing 100084, China.
authors: Chunhong
authors: C
authors: Zhao
authors: Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 0A2, Canada.
authors: Guanhui
authors: G
authors: Cheng
authors: State Key Laboratory of Hydroscience and Engineering, Dept. of Hydraulic Engineering, Tsinghua University, Beijing 100084, China.
authors: Pengfei
authors: P
authors: Huang
mesh_terms: D000383:Agriculture; D002681:China; D055864:Droughts; D004784:Environmental Monitoring; D040362:Geographic Information Systems; D060587:Groundwater; D006801:Humans; D008962:Models, Theoretical; D014881:Water Supply
keywords: Autoregressive error model; Groundwater level modeling; Hexi Corridor; SOM; Stepwise cluster inference; Uncertainty