Multivariate adaptive regression spline ensembles for management of multilayered coastal aquifers
Journal Publication ResearchOnline@JCUAbstract
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. (C) 2017 American Society of Civil Engineers.
Journal
Journal of Hydrologic Engineering
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Volume
22
ISBN/ISSN
1943-5584
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Issue
9
Pages Count
13
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Publisher
American Society of Civil Engineers
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DOI
10.1061/(ASCE)HE.1943-5584.0001550