Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

Computational Intelligence and Neuroscience
George KopsiaftisAristotelis Mantoglou

Abstract

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube s...Continue Reading

References

Mar 1, 2018·Computational Intelligence and Neuroscience·Athanasios VoulodimosEftychios Protopapadakis

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Citations

Mar 4, 2020·Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA·Zeynep Ceylan

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Software Mentioned

GPR
TreeEns
MODFLOW
SEAWAT
MT3DMS
BayesOpt

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