Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

Ozgur Kisi (), Levent Latifoğlu () and Fatma Latifoğlu ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 12, 4045-4057

Abstract: In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month’s monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE = 0.0132, MAE = 0.0883 and R = 0.8012 statistics, respectively. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Stream flow data; Empirical mode decomposition; Artificial neural networks; Forecasting (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11269-014-0726-8

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