A comparative study of Kriging and deep learning methods for shallow groundwater level estimation:A case study of the Shenzhen-Shanwei Spe-cial Cooperation Zone
A comparative study of Kriging and deep learning methods for shallow groundwater level estimation:A case study of the Shenzhen-Shanwei Spe-cial Cooperation Zone
[Objective]Knowledge of the regional groundwater level is an important foundation for groundwater re-source 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 pre-diction,but their applicability and robustness lack comparative analysis.[Methods]In this paper,spatial interpo-lation of groundwater levels in the Shenzhen-Shanwei Special Cooperation Zone was carried out using ordinary Krig-ing,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.[Results]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 informa-tion,was significantly better than the ordinary Kriging method and the deep learning method.In addition,the pre-dicted 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.[Conclusion]The results show that when the observation data are sparse,the prediction effect of coKriging with elevation information is significant-ly 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.
关键词
地下水位/克里金方法/深度学习方法/深汕特别合作区
Key words
groundwater level/Kriging method/deep learning method/Shenzhen-Shanwei Special Cooperation Zone
维普
万方数据