| Citation: | WANG Quanjuan, SUN Jingfeng, YANG Yingjie, ZHANG Jiangwei, WANG Guoliang, MA Enze, LIU Jin, ZHAO Xin. A comparative study of Kriging and deep learning methods for shallow groundwater level estimation: A case study of the Shenzhen-Shanwei Special Cooperation Zone[J]. Bulletin of Geological Science and Technology, 2024, 43(4): 291-301. doi: 10.19509/j.cnki.dzkq.tb20230192
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Knowledge of the regional groundwater level is an important foundation for groundwater resource evaluation and protection. Due to the limited amount of groundwater level data available at the regional scale, Kriging interpolation and deep learning methods are gradually being used for regional groundwater level prediction, but their applicability and robustness lack comparative analysis.
In this paper, spatial interpolation of groundwater levels in the Shenzhen-Shanwei Special Cooperation Zone was carried out using ordinary Kriging, coKriging and deep learning methods to explore the potential of these three methods for practical application to regional groundwater level prediction. To investigate the effect of the training set sample size on the prediction effect of the three methods, 239 monitoring wells were divided into two groups of 76 and 163 wells for the training of the three models.
The results showed that the RMSEs were 6.09, 4.04, and 7.11 when the training data of 76 wells were used to fit the validation set, and the Kriging method, which accounts for surface elevation information, was significantly better than the ordinary Kriging method and the deep learning method. In addition, the predicted water level distribution improved when a larger number of samples was used to predict the water level in the region. However, the spatial distribution characteristics still differed significantly.
The results show that when the observation data are sparse, the prediction effect of coKriging with elevation information is significantly greater than that of ordinary Kriging and deep learning methods, while the RMSEs obtained by the three methods are similar when the amount of observation data increases to a certain amount.
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