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

Xike Zhang,Zhiping Nie,Gui Zhang,Zifan Gui,Qiuwen Zhang

doi:10.3390/w10040416

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.

Highlights

Water is the source of life and is an indispensable part of life for drinking, irrigating and generating electricity, and so forth [1]
To evaluate the performance of the proposed hybrid ensemble empirical mode decomposition (EEMD)-Elman neural network (ENN) model, the five main statistical indices are used as the evaluation indicators in this study, which have been widely and commonly used for evaluating the performance of hydrological simulation and hydroclimate models
In order to improve the prediction precision of annual runoff time series, we proposed a hybrid prediction model based on EEMD and three-layer ENN methods in this study and the hybrid model is applied to the annual runoff time series of the four hydrological stations (i.e., Xiangtan station, Taojiang station, Taoyuan station and Shimen station) in the lower reach of four main rivers in the Dongting Lake basin and one (i.e., Chenglingji station) at the outlet of the lake, central China

Summary

Introduction

Water is the source of life and is an indispensable part of life for drinking, irrigating and generating electricity, and so forth [1]. And reliably predicting hydrological runoff time series plays an import role in the modern water resources management (i.e., water supply planning, water projects designing, hydropower generation, irrigation systems, water quality management, sustainable water resources utilization, eco-environment protections and biodiversity conservation, etc.) of a river basin [2,3]. As the result of a dramatic and continuous increase of rapid domestic economic development, population growth, and industrial, commercial, residential and agricultural demands, runoff prediction has attracted great attention from global hydrology scientists for improving. Accurate annual runoff time series model should be constructed to overcome these challenges

Methods

Results

Conclusion