Selection of Meta-models to Predict Saltwater Intrusion in Coastal Aquifers Using Entropy Weight Based Decision Theory

Right choice of meta-models is one of the most important factors determining the accuracy of predicting seawater intrusion phenomena in the aquifers of coastal belts. In this paper, entropy weight based decision theory is applied to rank the performances of meta-models. Six meta-models trained and validated by a set of input-output training patterns generated from a unified flow and solute transport model for saltwater intrusion are considered. Entropy weights are assigned to performance evaluation indicators in order to decide on the comparative significance of the indicators in meta-model performance. Meta-models are then ranked by incorporating this relative importance of individual performance indicators. This method of ranking provides reliability in meta-model selection by considering a set of performance indicators instead of relying on a single indicator. Furthermore, this method is compared with variation coefficient weighting method. It is shown that the proposed entropy weight based ranking methodology can be successfully applied to select the best meta-model for predicting seawater intrusion processes in coastline aquifers.