relevance vector machine

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Applicability of statistical learning algorithms in groundwater quality modeling

Journal Title, Volume, Page: 
Water Resources Research (41) W05010. doi:10.1029/2004WR003608
Year of Publication: 
2005
Authors: 
Abedalrazq Khalil
Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, Utah, USA
Mohammad N. Almasri
Water and Environmental Studies Institute, An-Najah National University, Nablus, West Bank
Current Affiliation: 
Department of Civil Engineering, College of Engineering, An-Najah National University, P. O. Box 7, Nablus, Palestine
Mac McKee
Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, Utah, USA
Jagath J. Kaluarachchi
Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, Utah, USA
Preferred Abstract (Original): 
Four algorithms are outlined, each of which has interesting features for predicting contaminant levels in groundwater. Artificial neural networks (ANN), support vector machines (SVM), locally weighted projection regression (LWPR), and relevance vector machines (RVM) are utilized as surrogates for a relatively complex and time-consuming mathematical model to simulate nitrate concentration in groundwater at specified receptors. Nitrates in the application reported in this paper are due to on-ground nitrogen loadings from fertilizers and manures. The practicability of the four learning machines in this work is demonstrated for an agriculture-dominated watershed where nitrate contamination of groundwater resources exceeds the maximum allowable contaminant level at many locations. Cross-validation and bootstrapping techniques are used for both training and performance evaluation. Prediction results of the four learning machines are rigorously assessed using different efficiency measures to ensure their generalization ability. Prediction results show the ability of learning machines to build accurate models with strong predictive capabilities and hence constitute a valuable means for saving effort in groundwater contamination modeling and improving model performance.
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