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Examining Efficacy of Metamodels in predicting Ground Water Table

Volume 11, Number 3, May 2015 - Paper 7 - pp. 275-281


1 Assistant Professor (Junior), School of Information Technology & Engineering, VIT University, Vellore-632014, INDIA.
2 Professor, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA.
3 Professor, Center for Disaster Mitigation and Management, VIT University, Vellore-632014, INDIA.

(Received on July 07, 2014, revised on November 24, 2014 and February 23, 2015)


This article examines the capability of Gaussian Process Regression (GPR), Generalized Regression Neural Network (GRNN) and Relevance Vector Machine (RVM) for prediction of Ground Water Table (dw) at Vellore (India). RVM, GRNN and GPR have been adopted as regression techniques.  RVM is a probabilistic model. GRNN approximates any arbitrary function between input and output variables. GPR is a non-parametric model. The developed GPR, RVM and GRNN give the spatial variability of dw at Vellore. Map of dw has been also produced by the GPR, RVM and GRNN models. The results show that the developed RVM gives the best model for prediction of dw at Vellore.


References: 18

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