Groundwater Level Prediction with Deep Learning Methods

Abstract

The development of civilization and the preservation of environmental ecosystems are strongly dependent on water resources. Typically, an insufficient supply of surface water resources for domestic, industrial, and agricultural needs is supplemented with groundwater resources. However, groundwater is a natural resource that must accumulate over many years and cannot be recovered after a short period of recharge. Therefore, the long-term management of groundwater resources is an important issue for sustainable development. The accurate prediction of groundwater levels is the first step in evaluating total water resources and their allocation. However, in the process of data collection, data may be lost due to various factors. Filling in missing data is a main problem that any research field must address. It is well known that to maintain data integrity, one effective approach is missing value imputation (MVI). In addition, it has been demonstrated that machine learning may be a better tool. Therefore, the main purpose of this study was to utilize a generative adversarial network (GAN) that consists of a generative model and a discriminative model for imputation. Although the GAN could not capture the groundwater level endpoints in every section, the overall simulation performance was still excellent to some extent. Our results show that the GAN can improve the accuracy of water resource evaluations. In the current study, two interdisciplinary deep learning methods, univariate and Seq2val (sequence-to-value), were used for groundwater level estimation. In addition to addressing the significance of the parameter conditions, the advantages and disadvantages of these two models in hydrological simulations were also discussed and compared. Regarding parameter selection, the simulation results for univariate analysis were better than those for Seq2val analysis. Finally, univariate was employed to examine the limits of the models in long-term water level simulations. Our results suggest that the accuracy of CNNs is better, while LSTM is better for the simulation of multistep prediction. Therefore, the interdisciplinary deep learning approach may be beneficial for providing a better evaluation of water resources.