A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer

Volume: 396, Issue: 1, Pages: 128 - 138
Published: Jan 5, 2011
Abstract
Summary null null We have developed two nonlinear time-series models for predicting groundwater level (GWL) fluctuations using artificial neural networks (ANNs) and support vector machines (SVMs). The models were applied to GWL prediction of two wells at a coastal aquifer in Korea. Among the possible variables (past GWL, precipitation, and tide level) for an input structure, the past GWL was the most effective input variable for this study site....
Paper Details
Title
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
Published Date
Jan 5, 2011
Volume
396
Issue
1
Pages
128 - 138
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