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

Zhang, Xike; Zhang, Qiuwen; Gui, Zhilun; Nie, Zhi-ping; Gui, Zifan

doi:10.3390/w10040416

Cited by 34 publications

(35 citation statements)

References 43 publications

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“…However, these models require that the time series be stationary and have a large number of data points for a robust forecasting result. Nonlinear data-driven models, such as the Artificial Neural Network (ANN), with its advantage of learning and identifying complex data patterns with less data, has captured significant attention in precipitation, rainfall, runoff, drought, evapotranspiration and temperature forecasting problems in the past few years [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. However, one of the major challenges faced by ANN is that it requires an iterative adjustment of model parameters, a slow response of the gradient-based learning algorithm used, and a relatively low prediction accuracy compared with more advanced NN algorithms [ 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [ 30 ] proposed a hybrid model that utilized the EEMD coupled with ANN for long-term runoff forecasting. Zhang et al [ 31 ] adopted the EEMD coupled with the Elman Neural Network (ENN) for annual runoff time series forecasting. Their research results demonstrated that the EEMD coupled with other popular methods can significantly improve time series forecasting precision compared with some other popular methods.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Zhang

Gui

et al. 2018

IJERPH

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Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Zhang

Gui

et al. 2018

IJERPH

Self Cite

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“…They additionally proposed an ANN-based model designed to overcome this limitation and listed the improved features of the developed model for irregular rainfall-runoff prediction based on the results obtained by applying the model to catchments in the Mediterranean region. Zhang et al [14] used an Elman neural network to improve the prediction accuracy of the time-series runoff input data by taking into account the nonlinear characteristics of the data. They proposed a water level prediction model for warning purposes by considering the hidden layer for lead time (t − 1) along with the originally predicted time (t).…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Yangho

Park²,

Lee

et al. 2019

Water

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The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) and the HEC's River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

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“…Niu et al [33] applied the EEMD combined with LSSVM for day-ahead PM2.5 concentration prediction and obtained accurate results. Zhang et al [34] proposed a new hybrid model, EEMD with Elman neural network, for annual runoff time series forecasting in the Dongting Lack basin, and the results showed that this proposed model gave a good performance. The hybrid forecasting model formed by combining VMD with other algorithms has been successfully applied in many fields.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Yang

2018

Information

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Abstract:The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan'an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

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

Cited by 34 publications

References 43 publications

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

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