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

  • Heesung Yoon Korea Institute of Geoscience and Mineral Resources, Daejeon, Korea, Republic of.
  • Yongcheol Kim | yckim@kigam.re.kr 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.

Abstract

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.

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Published
2019-03-26
Keywords:
time series models, groundwater level, artificial neural network, support vector machine
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How to Cite
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