When using a simulation model to study seawater intrusion (SI), uncertainty in the parameters directly affects the results. The impact of the rise in sea levels due to global warming on SI cannot be ignored. In this paper, the Monte Carlo method is used to analyze the uncertainty in modeling SI. To reduce the computational cost of the repeated invocation of the simulation model as well as time, a surrogate model is established using a radial basis function (RBF)-based neural network method. To enhance the accuracy of the substitution model, input samples are sampled using the Latin hypercube sampling (LHS) method. The results of uncertainty analysis had a high reference value and show the following: (1) The surrogate model created using the RBF method can significantly reduce computational cost and save at least 95% of the time needed for the repeated invocation of the simulation model while maintaining high accuracy. (2) Uncertainty in the parameters and the magnitude of the rise in sea levels have a significant impact on SI. The results of prediction were thus highly uncertain. In practice, it is necessary to quantify uncertainty to provide more intuitive predictions.
Keywords: RBF neural network; Sea level rise; Seawater intrusion; Surrogate model; Uncertainty analysis.