Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition

Wang, Wenchuan; Chau, Kwok Wing; Qiu, Lin; Chen, Yang bo

doi:10.1016/j.envres.2015.02.002

Cited by 203 publications

(84 citation statements)

References 48 publications

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“…Third, as the preprocessing techniques of the MLMs, time series decomposition methods have been applied to hybrid MLMs development. The methods included discrete wavelet transform (DWT) [37,38], maximal overlap DWT (MODWT) [39], wavelet packet transform (WPT) [40], empirical mode decomposition (EMD) [41,42], and ensemble EMD (EEMD) [43,44]. It has been reported that these hybrid MLMs, which consists of time series decomposition and sub-time series modeling, were able to achieve better performance compared with the single MLMs.…”

Section: Introductionmentioning

confidence: 99%

“…Huang et al [42] assessed the performance of a modified EMD-SVMs model to forecast monthly streamflow and confirmed that the hybrid model provided high prediction accuracy and reliable stability. Wang et al [43] developed an ANN modeling approach based on EEMD to forecast medium-and long-term runoff. They confirmed that the EEMD was able to increase the forecasting accuracy effectively.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…It was shown that the hybrid model exhibited the best predictive performance compared to ARIMA and singular spectrum analysis-linear recurrent formulae (SSA-LRF) models. Wang et al [17] presented an artificial neural network (ANN) model coupled with ensemble empirical mode decomposition (EEMD) for forecasting medium-and long-term runoff time series, and the proposed EEMD-ANN model attained a significant improvement over the ANN approach alone in medium-and long-term runoff time series forecasting. Recently, the Bayesian model averaging (BMA)-based multimodel has gained popularity as a multimodel because it can provide a more realistic forecast that considers both between-model variances and in-model variances.…”

Section: Introductionmentioning

confidence: 99%

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

Chen

Wei

et al. 2017

Water

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Medium-and long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was developed to further improve the accuracy and reliability of runoff forecasting fully considering of large-scale and local-scale climatic factors. First, with four large-scale atmospheric oscillations, sea surface temperature, precipitation, and temperature as the predictors, multiple linear regression (MLR), radial basis function neural network (RBFNN), and support vector regression (SVR) models were built. Next, a Bayesian model averaging (BMA)-based multimodel was developed using weighted MLR, RBFNN, and SVR models, and the performance of the BMA-based multimodel was compared to those of the MLR, RBFNN, and SVR models. Finally, the high-runoff performance of these four models was further analyzed to prove the effectiveness of each model. The BMA-based multimodel performed better than those of the other models, as well as high-runoff forecasting. The results also revealed that the performance of the forecasting models with multiple climatic factors were generally superior to that without climatic factors. The BMA-based multimodel with climatic factors not only provides a promising, reliable method for medium-and long-term runoff forecasting, but also facilitates uncertainty estimation under different confidence intervals.

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“…Artificial neural network have been successfully applied to solve hard and complex problems in the field of industry and research. A huge number of publications have proved the strength of ANN in the medical field [16] [17].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

Turabieh¹

2016

AJOR

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The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local search one. The proposed algorithm implies the ability of ANN to find a relationship between the input and the output variables while the stochastic search ability of GWO is used for finding the initial optimal weights and biases of the ANN to reduce the probability of ANN getting stuck at local minima and slowly converging to global optimum. For evaluation purpose, the performance of hybrid model (ANN-GWO) was compared with standard back-propagation neural network (BPNN) using Root Mean Square Error (RMSE). The results demonstrate that the proposed model increases the convergence speed and the accuracy of prediction.

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Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition

Cited by 203 publications

References 48 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Improved Medium- and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information

A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

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