Groundwater contamination source identification based on a hybrid particle swarm optimization-extreme learning machine

When the simulation-optimization method is applied to groundwater contamination source identification (GCSI), the numerical simulation model is usually embedded in the optimization model as a constraint condition. Hundreds and thousands of simulation model calls, while solving the optimization model, often lead to computational disaster. Establishing a surrogate model for the simulation model can effectively alleviate this drawback. When using extreme learning machine (ELM) to establish a surrogate model for a simulation model, the output weights are calculated using randomly given input weights and hidden-layer deviations, which may lead to inadequate accuracy of the surrogate model and insufficient generalization ability to samples not appearing in the training set. However, by combining the ELM with particle swarm optimization (PSO), the PSO can be used to optimize the selection of the input weights and hidden-layer deviations of the ELM; thus, calculating the output weights and establishing a surrogate model based on the PSO-ELM instead of the simulation model embedded in the optimization model to complete the GCSI. The uncertainty of the parameters is rarely considered when the simulation-optimization method is applied to GCSI. Thus, the uncertainty of the parameters is considered in GCSI based on the PSO-ELM. The results show that compared with the ELM, the PSO-ELM can establish the surrogate model with higher accuracy. The surrogate model based on PSO-ELM can be embedded in the optimization model to effectively solve GCSI problems. The predictive model based on PSO-ELM can predict the release histories of contamination sources corresponding to different random parameters.