The Effect of Seasonal Input on Predicting Groundwater Level Using Artificial Neural Network

인공신경망을 이용한 지하수위 예측과 계절효과 반영을 위한 입력치의 영향

  • Kim, Incheol (School of Civil and Environmental Engineering, Yonsei University) ;
  • Lee, Junhwan (School of Civil and Environmental Engineering, Yonsei University)
  • 김인철 (연세대학교 사회환경시스템공학과) ;
  • 이준환 (연세대학교 사회환경시스템공학과)
  • Received : 2018.09.11
  • Accepted : 2018.09.18
  • Published : 2018.09.30


Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.


Supported by : 한국연구재단(NRF), 한국에너지기술평가원


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