Simulation of Seawater Intrusion Area Using Feedforward Neural Network in Longkou, China

Abstract

Reliable simulation of seawater intrusion (SI) is necessary for sustainable groundwater utilization. As a powerful tool, feedforward neural network (FNN) was applied to study seawater intrusion area (SIA) fluctuations in Longkou, China. In the present study, changes of groundwater level (GWL) were modeled by FNN Model 1. Then, FNN Model 2 was developed for fitting the relationship between GWL and SIA. Finally, two models were integrated to simulate SIA changes in response to climatic and artificial factors. The sensitivity analysis of each impact factor was conducted by the “stepwise” method to quantify the relative importance for SIA and GWL. The results from the integrated model indicated that this method could accurately reproduce SIA fluctuations when the Nash–Sutcliffe efficiency coefficient was 0.964, the root mean square error was 1.052 km2, the correlation coefficient was 0.983, and the mean absolute error was 0.782 km2. The results of sensitivity analysis prove that precipitation and groundwater pumping for agriculture mainly affect fluctuations of SIA in the study area. It can be concluded that FNN is effectively used for modeling SI fluctuations together with GWL, which can provide enough support for the sustainable management of groundwater resources with consideration of crucial impact factors of seawater intrusion (SI).