LAUSR

Seawater intrusion monitoring is quite different from the conventional monitoring of groundwater pollution. In this study, a new optimization method for the seawater intrusion monitoring scheme in the transitional zone was proposed. The objective of optimization was to maximize effective information monitored. The structural similarity index method (SSIM) of the image quality assessment was innovatively used to establish a mathematical expression for the effective monitored information, and an optimization model was constructed based on this. Taken the Longkou city of China as the study area, a numerical simulation model of variable density groundwater was constructed. The Monte Carlo method was used to consider the influence of the sensitivity parameters uncertainty on the monitoring scheme design. To avoid repeatedly calling of simulation models in the process of Monte Carlo experiments, a surrogate model was constructed by using the kernel extreme learning machine (KELM). Finally, the optimization model was solved by the genetic algorithm to obtain the optimal monitoring scheme. The results showed that the input-output relationship of the numerical simulation model for variable-density groundwater can be well approximated by the KELM surrogate model. The monitoring scheme optimized by the above method can well reflect the real state of seawater intrusion. This study expands the method on the scheme designs for seawater intrusion monitoring.