Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Soil moisture prediction using a support vector regression

  • Lee, Danhyang (Department of Statistics, Kyungpook National University) ;
  • Kim, Gwangseob (Department of Civil Engineerings, Kyungpook National University) ;
  • Lee, Kyeong Eun (Department of Statistics, Kyungpook National University)
  • Received : 2013.02.23
  • Accepted : 2013.03.25
  • Published : 2013.03.31
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Abstract

Soil moisture is a very important variable in various area of hydrological processes. We predict the soil moisture using a support vector regression. The model is trained and tested using the soil moisture data observed in five sites in the Yongdam dam basin. With respect to soil moisture data of of four sites-Jucheon, Bugui, Sangieon and Ahncheon which are used to train the model, the correlation coefficient between the esimtates and the observed values is about 0.976. As the result of the application to Cheoncheon2 for validating the model, the correlation coefficient between the estimates and the observed values of soil moisture is about 0.835. We compare those results with those of artificial neural network models.

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References

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