Surrogate Model-Based Simulation-Optimization Approach for Groundwater Source Identification Problems

摘要

This study investigates and discusses a time-efficient technology that contains a surrogate model within a simulation-optimization model to identify the characteristics of groundwater pollutant sources. In the proposed surrogate model, Latin hypercube sampling (a stratified sampling approach) and artificial neural network (commencing at the stress period when the concentration is within a certain range, and ending at the peak time) were utilized to reduce workload and costly computing time. The results of a comparison between the proposed surrogate model and the common artificial neural network model and non-surrogate model indicated that the proposed model is a time-efficient technology which could be used to solve groundwater source identification problems.