A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction

Sustainable groundwater resource management necessitates dependable and precise predictions of groundwater head fields under fluctuating climatic conditions. The substitution of original simulation models with efficient surrogates presents a challenge in simultaneously accounting for correlations among multiple time series outputs and maintaining overall prediction accuracy. This study develops a novel surrogate modelling approach, DSCNN-GRU, incorporating a deep separable convolutional neural network (DSCNN) and a gated recurrent unit (GRU), to efficiently capture temporal and spatial variations in groundwater head fields from transient groundwater flow models using input hydraulic conductivity field data. The applicability and performance of the proposed method are evaluated for predicting groundwater head fields in a practical research area under three scenarios with different hydraulic conductivity fields. The performance of the DSCNN-GRU model is compared to the traditional convolutional neural network (CNN), CNN-LSTM, and DSCNN-LSTM models to further test its applicability. The numerical study demonstrates that optimizing hyperparameters can result in reasonably accurate performance of the proposed model, and the “simplest” DSCNN-GRU outperforms CNN, CNN-LSTM, and DSCNN-LSTM in both prediction accuracy and time-to-solution.