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

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

Authors :
Heesung Yoon
Yongcheol Kim
Kyoochul Ha
Soo-Hyoung Lee
Gee-Pyo Kim
Source :
Water (20734441); May2017, Vol. 9 Issue 5, p323, 16p
Publication Year :
2017

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734441
Volume :
9
Issue :
5
Database :
Complementary Index
Journal :
Water (20734441)
Publication Type :
Academic Journal
Accession number :
123250368
Full Text :
https://doi.org/10.3390/w9050323