A DenseNet Network Based Surrogate Model for Simulating Contaminant Transport in Groundwater Systems

Abstract:

Groundwater inverse problems such as groundwater contaminant identification and aquifer parameter inversion problems are usually restricted by the computational load. In order to reduce the computational cost of groundwater inversion， the surrogate method is a feasible solution. In this paper， imitating the image recognition process of the convolutional neural network， the groundwater flow movement and contaminant transport problem is transformed into an image regression problem of the functional relationship between input image （hydraulic conductivity field， pollution source information， etc.） and output image （groundwater level， contaminant concentration， etc.）. The surrogate model of groundwater flow movement and contaminant transport is constructed by using AR-Net-WL based on the DenseNet network. The case study shows that， for the overfitting of the surrogate model， a 10% improvement in accuracy can be obtained by selecting training samples as large as possible. When there are no conditions to increase the training sample， AR-Net-WL with an optimal regularization coefficient can also achieve a good performance with fewer training samples （500 training samples） and can accurately predict the groundwater flow movement and contaminant transport.