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http://dx.doi.org/10.17820/eri.2018.5.3.125

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

Kim, Incheol (School of Civil and Environmental Engineering, Yonsei University)
Lee, Junhwan (School of Civil and Environmental Engineering, Yonsei University)
Publication Information
Ecology and Resilient Infrastructure / v.5, no.3, 2018 , pp. 125-133 More about this Journal
Abstract
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.
Keywords
Artificial neural network; Hydrological factor; Performance; Seasonal effect;
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Times Cited By KSCI : 1  (Citation Analysis)
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