Influence of the range of data on the performance of ANN- and SVM- based time series models for reproducing groundwater level observations


Submitted: 22 January 2019
Accepted: 19 March 2019
Published: 26 March 2019
Abstract Views: 897
PDF: 394
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Authors

  • Heesung Yoon Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea, Republic of.
  • Yongcheol Kim Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea, Republic of.
  • Soo-Hyoung Lee Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea, Republic of.
  • Kyoochul Ha Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea, Republic of.

In the present study, we designed time series models for predicting groundwater level fluctuations using an artificial neural network (ANN) and a support vector machine (SVM). To estimate the model sensitivity to the range of data set for the model building, numerical tests were conducted using hourly measured groundwater level data at a coastal aquifer of Jeju Island in South Korea. The model performance of the two models is similar and acceptable when the range of input variable lies within the data set for the model building. However, when the range of input variables is beyond it, both the models showed abnormal prediction results: an oscillation for the ANN model and a constant value for SVM. The result of the numerical tests indicates that it is necessary to obtain various types of input and output variables and assign them to the model building process for the success of design time series models of groundwater level prediction.


Supporting Agencies

This research was supported by the Basic Research Project(19-3411) of the Korea Institute of Geoscience and Mineral Resources and ‘Development of drought overcoming technology based on Well Network System’ Project funded by Ministry of Environment, Republic of Korea.

Yoon, H., Kim, Y., Lee, S.-H., & Ha, K. (2019). Influence of the range of data on the performance of ANN- and SVM- based time series models for reproducing groundwater level observations. Acque Sotterranee - Italian Journal of Groundwater, 8(1). https://doi.org/10.7343/as-2019-376

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