An evolutionary multiobjective strategy for the effective management of groundwater resources

This paper introduces a modeling approach aimed at the management of groundwater resources based on a hybrid multiobjective paradigm, namely Evolutionary Polynomial Regression. Multiobjective modeling in hybrid evolutionary computing enables the user (a) to find a set of feasible symbolic models, (b) to make a robust choice of models and (c) to improve computational efficiency, simultaneously developing a set of models with diverse structural parsimony levels. Moreover, this methodology appears to be well suited to those cases where process input and the boundary conditions are not easily accessible. The multiobjective approach is based on the Pareto dominance criterion and it is fully integrated into the Evolutionary Polynomial Regression paradigm. This approach proves to be effective for modeling groundwater systems, which usually requires (a) accurate analyses of the underlying physical phenomena, (b) reliable forecasts under different hypothetical scenarios and (c) good generalization features of the models identified. For these reasons it is important to construct easily interpretable models which are specialized for well defined purposes. The proposed methodology is tested on a case study aimed at determining the dynamic relationship between rainfall depth and water table depth for a shallow unconfined aquifer located in southeast Italy.