Multivariate Adaptive Regression Spline Ensembles for Management of Multilayered Coastal Aquifers

Abstract

Application of multivariate adaptive regression spline ensembles (En-MARS) in a coupled simulation-optimization methodology to derive multiple-objective optimal groundwater extraction strategies for a multilayered coastal aquifer system is demonstrated. Two conflicting objectives of groundwater extraction strategies are solved using a controlled elitist multiobjective genetic algorithm. A three-dimensional density-dependent coupled flow and salt-transport numerical simulation model is used to generate the training patterns of groundwater extraction strategies and resulting saltwater concentrations. Prediction capability of En-MARS is compared with that of the best multivariate adaptive regression spline (MARS) model in the ensemble. En-MARS is then linked externally within the optimization algorithm to develop the management model. The optimal solutions obtained from the En-MARS models are verified by running the numerical simulation model. The results indicate that MARS-based ensemble modeling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. The adaptive nature of MARS models and use of ensembles and parallel processing results in a computationally efficient, accurate, and reliable methodology for coastal aquifer management that also incorporates uncertainties in modeling.