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Water Resources Management

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01-09-2014

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

Authors: Ozgur Kisi, Levent Latifoğlu, Fatma Latifoğlu

Published in: Water Resources Management | Issue 12/2014

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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.

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Title: Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series
Authors: Ozgur Kisi
Levent Latifoğlu
Fatma Latifoğlu
Publication date: 01-09-2014
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 12/2014
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-014-0726-8

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