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QI Yongqiang, LI Wenpeng, ZHENG Yuejun, LI Hui, WANG Chengjian. Application of machine learning to aquifer analyses：Locating hydrogeological boundaries with water table monitoring data[J]. Hydrogeology & Engineering Geology, 2022, 49(1): 1-11. doi: 10.16030/j.cnki.issn.1000-3665.202104030
Citation: QI Yongqiang, LI Wenpeng, ZHENG Yuejun, LI Hui, WANG Chengjian. Application of machine learning to aquifer analyses：Locating hydrogeological boundaries with water table monitoring data[J]. Hydrogeology & Engineering Geology, 2022, 49(1): 1-11. doi: 10.16030/j.cnki.issn.1000-3665.202104030
shu

Application of machine learning to aquifer analyses：Locating hydrogeological boundaries with water table monitoring data

  • 1.

    Beijing Water International Co. Ltd. , Beijing　102206, China

  • 2.

    China Institute of Geo-Environment Monitoring, Beijing　100081, China

  • 3.

    Hydrological Bureau of Qingdao, Qingdao, Shandong　266071, China

Received Date:  15 March 2021
Revised Date:  17 May 2021
Publish Date:  15 January 2022
    Abstract

  • Groundwater level fluctuations in China are being monitored with unprecedented frequency and density, which drives the need for mining such types of data. In a typical aquifer analysis project, groundwater level data is generally applied after the completion of the aquifer conceptual framework. When the temporal and spatial density of groundwater level data gradually increases, the information gain needs to be effectively transformed into conceptual knowledge of the model. In this study, we propose a method to identify hydrological boundaries based on the groundwater level monitoring data. In this method we discretize space into a triangular mesh using monitoring wells as the initial nodes, and a transformation function gradF is defined to calculate the hydraulic gradient at any given location on the mesh. The hydraulic gradient field is subsequently use to drive an array of randomly scattered particles to obtain the streamline representation of the flow field, which will in turn serve as the basis for deducing and refining the recharge and discharge boundaries of a hydrogeological domain. This method is implemented into the geo-environmental scientific computation platform (EnviFusion-CGS), and a detailed work flow is developed to facilitate the development of the aquifer conceptual model. This method is applied to the hydrogeological investigation of the Dagu aquifer located in Qingdao of Shandong Province, where the spatial distributions and dynamic fluctuations of the hydrogeological boundaries are identified.

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