Next Article in Journal
The Suitability of Pozzolan as Admixing Aggregate for Fe0-Based Filters
Previous Article in Journal
Planning Water Resources in an Agroforest Ecosystem for Improvement of Regional Ecological Function Under Uncertainties
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Water 2018, 10(4), 416; https://doi.org/10.3390/w10040416

A Hybrid Model for Annual Runoff Time Series Forecasting Using Elman Neural Network with Ensemble Empirical Mode Decomposition

1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
School of Municipal and Mapping Engineering, Hunan City University, Yiyang 413000, China
3
Key Laboratory for Digital Dongting Lake basin of Hunan Province, Central South University of Forestry and Technology, Changsha 410004, China
4
Shenzhen Garden Management Center, Shenzhen 518000, China
*
Authors to whom correspondence should be addressed.
Received: 28 February 2018 / Revised: 30 March 2018 / Accepted: 30 March 2018 / Published: 2 April 2018
(This article belongs to the Section Hydrology)
View Full-Text   |   Download PDF [42147 KB, uploaded 3 May 2018]   |  

Abstract

Because of the complex nonstationary and nonlinear characteristics of annual runoff time series, it is difficult to achieve good prediction accuracy. In this paper, ensemble empirical mode decomposition (EEMD) coupled with Elman neural network (ENN)—namely the EEMD-ENN model—is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The annual runoff time series from four hydrological stations in the lower reaches of the four main rivers in the Dongting Lake basin, and one at the outlet of the lake, are used as a case study to test this new hybrid model. First, the nonstationary and nonlinear original annual runoff time series are decomposed to several relatively stable intrinsic mode functions (IMFs) by using EEMD. Then, each IMF is predicted by using ENN. Next, the predicted results of each IMF are aggregated as the final prediction results for the original annual runoff time series. Finally, five statistical indices are adopted to measure the performance of the proposed hybrid model compared with a back propagation (BP) neural network, EEMD-BP, and ENN models—mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (R) and Nash–Sutcliffe coefficient of efficiency (NSCE). The performance comparison results show that the proposed hybrid model performs better than the BP, EEMD-BP or ENN models. In short, the developed hybrid model can provide a significant improvement in annual runoff time series forecasting. View Full-Text
Keywords: ensemble empirical mode decomposition (EEMD); Elman neural network (ENN); hybrid model; annual runoff; time series forecasting; Dongting Lake basin ensemble empirical mode decomposition (EEMD); Elman neural network (ENN); hybrid model; annual runoff; time series forecasting; Dongting Lake basin
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Zhang, Q.; Zhang, G.; Nie, Z.; Gui, Z. A Hybrid Model for Annual Runoff Time Series Forecasting Using Elman Neural Network with Ensemble Empirical Mode Decomposition. Water 2018, 10, 416.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top