Roy, Dilip Kumar, and Datta, Bithin (2019) An ensemble meta-modelling approach using the Dempster-Shafer theory of evidence for developing saltwater intrusion management strategies in coastal aquifers. Water Resources Management, 33 (2). pp. 775-795.

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Abstract

The optimum abstraction policy of coastal groundwater resources is prescribed by solving a meta-model based saltwater intrusion management model. Groundwater parameter uncertainties are explicitly incorporated into the developed meta-models in order to address the uncertainties present in coastal aquifer processes. Nevertheless, the accuracy and consequent reliability of such a management model depends upon the right choice of meta-models or a combination of meta-models. The optimal combination of meta-models, also referred to as an ensemble meta-model, can be selected by applying the Dempster-Shafer (D-S) theory of evidence. D-S evidence theory provides a platform upon which to base the selection of the best meta-model or combination of meta-models to formulate the preferred ensemble. This study demonstrates the application of D-S theory to provide an ensemble of meta-models for developing saltwater intrusion management models in coastal aquifers. The prediction accuracy of the developed ensemble meta-model is compared with that of the best standalone meta-model in the ensemble. The results confirm that the ensemble meta-model performs equally well when compared with the best meta-model in the ensemble. The developed meta-models and their ensemble are then used to develop computationally feasible multiple objective saltwater intrusion management models by utilizing an integrated simulation-optimization approach. The solution results of the management models demonstrate the superiority of the ensemble meta-model approach over standalone meta-models in obtaining Pareto optimal groundwater abstraction patterns. The evaluation of the proposed methodology is demonstrated using an illustrative multilayer coastal aquifer system subjected to groundwater parameter uncertainties.

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