Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models

Surrogate model based methodologies are developed for evolving multi-objective management strategies for saltwater intrusion in coastal aquifers. Two different surrogate models based on genetic programming (GP) and modular neural network (MNN) are developed and linked to a multi-objective genetic algorithm (MOGA) to derive the optimal pumping strategies for coastal aquifer management, considering two objectives. Trained and tested surrogate models are used to predict the salinity concentrations at different locations resulting due to groundwater extraction. A two-stage training strategy is implemented for training the surrogate models. Surrogate models are initially trained with input patterns selected uniformly from the entire search space and optimal management strategies based on the model predictions are derived from the management model. A search space adaptation and model retraining is performed by identifying a modified search space near the initial optimal solutions based on the relative importance of the variables in salinity prediction. Retraining of the surrogate models is performed using input–output samples generated in the modified search space. Performance of the methodologies using GP and MNN based surrogate models are compared for an illustrative study area. The capability of GP to identify the impact of input variables and the resulting parsimony of the input variables helps in developing efficient surrogate models. The developed GP models have lesser uncertainty compared to MNN models as the number of parameters used in GP is much lesser than that in MNN models. Also GP based model was found to be better suited for optimization using adaptive search space.