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http://dx.doi.org/10.9720/kseg.2013.2.137

Application of groundwater-level prediction models using data-based learning algorithms to National Groundwater Monitoring Network data  

Yoon, Heesung (Korea Institute of Geoscience and Mineral Resources)
Kim, Yongcheol (Korea Institute of Geoscience and Mineral Resources)
Ha, Kyoochul (Korea Institute of Geoscience and Mineral Resources)
Kim, Gyoo-Bum (Geowater+ Research Center, K-water Institute)
Publication Information
The Journal of Engineering Geology / v.23, no.2, 2013 , pp. 137-147 More about this Journal
Abstract
For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.
Keywords
artificial neural network; support vector machine; rainfall; groundwater level; National Groundwater Monitoring Network;
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