Selection of meta-models to predict saltwater intrusion in coastal aquifers using entropy weight based decision theory

Roy, Dilip Kumar, and Datta, Bithin (2018) Selection of meta-models to predict saltwater intrusion in coastal aquifers using entropy weight based decision theory. In: Proceedings of the IEEE Conference on Technologies for Sustainability. From: SusTech 2018: 6th IEEE Conference on Technologies for Sustainability, 11-13 November 2018, Long Beach, CA, USA.

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Abstract

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.

Item ID: 57881
Item Type: Conference Item (Research - E1)
ISBN: 978-1-5386-7791-9
Keywords: saltwater intrusion; meta-model; entropy weight; coastal aquifers
Date Deposited: 11 Apr 2019 05:21
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 100%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 100%
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