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Water 2017, 9(5), 323; doi:10.3390/w9050323

Comparative Evaluation of ANN- and SVM-Time Series Models for Predicting Freshwater-Saltwater Interface Fluctuations

1
Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
2
Jeju Water Resources Headquarters, Jeju 63343, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Maurizio Polemio and Kristine Walraevens
Received: 21 February 2017 / Revised: 28 April 2017 / Accepted: 2 May 2017 / Published: 4 May 2017
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Abstract

Time series models based on an artificial neural network (ANN) and support vector machine (SVM) were designed to predict the temporal variation of the upper and lower freshwater-saltwater interface level (FSL) at a groundwater observatory on Jeju Island, South Korea. Input variables included past measurement data of tide level (T), rainfall (R), groundwater level (G) and interface level (F). The T-R-G-F type ANN and SVM models were selected as the best performance model for the direct prediction of the upper and lower FSL, respectively. The recursive prediction ability of the T-R-G type SVM model was best for both upper and lower FSL. The average values of the performance criteria and the analysis of error ratio of recursive prediction to direct prediction (RP-DP ratio) show that the SVM-based time series model of the FSL prediction is more accurate and stable than the ANN at the study site. View Full-Text
Keywords: artificial neural network; support vector machine; time series model; freshwater-saltwater interface; direct prediction; recursive prediction artificial neural network; support vector machine; time series model; freshwater-saltwater interface; direct prediction; recursive prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yoon, H.; Kim, Y.; Ha, K.; Lee, S.-H.; Kim, G.-P. Comparative Evaluation of ANN- and SVM-Time Series Models for Predicting Freshwater-Saltwater Interface Fluctuations. Water 2017, 9, 323.

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