Paper Title:
Groundwater Level Forecasting Based on Support Vector Machine
  Abstract

In this paper, we apply support vector regression (SVR) for groundwater level forecasting and compare its results to other prediction methods using real groundwater level data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector regression will perform well for time series analysis. Compared to other predictors, our results show that the SVR predictor can reduce significantly both relative mean errors and root mean squared errors of predicted groundwater level. We demonstrate the feasibility of applying SVR in groundwater level prediction and prove that SVR is applicable and performs well for groundwater data analysis.

  Info
Periodical
Edited by
Ran Chen
Pages
1365-1369
DOI
10.4028/www.scientific.net/AMM.44-47.1365
Citation
W. G. Zhao, H. Wang, Z. J. Wang, "Groundwater Level Forecasting Based on Support Vector Machine", Applied Mechanics and Materials, Vols. 44-47, pp. 1365-1369, 2011
Online since
December 2010
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