Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction
Joint Authors
Kopsiaftis, George
Protopapadakis, Eftychios
Voulodimos, Athanasios
Doulamis, Nikolaos
Mantoglou, Aristotelis
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-17
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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 sampling method is used to obtain the pumping rate patterns.
For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear).
A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion.
The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).
American Psychological Association (APA)
Kopsiaftis, George& Protopapadakis, Eftychios& Voulodimos, Athanasios& Doulamis, Nikolaos& Mantoglou, Aristotelis. 2019. Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129402
Modern Language Association (MLA)
Kopsiaftis, George…[et al.]. Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1129402
American Medical Association (AMA)
Kopsiaftis, George& Protopapadakis, Eftychios& Voulodimos, Athanasios& Doulamis, Nikolaos& Mantoglou, Aristotelis. Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129402
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129402