A hybrid EMD-SVR model for the short-term prediction of significant wave height

Duan, Wenyang; Han, Yang; Huang, Li‐Min; Zhao, Bo; Wang, M.H.

doi:10.1016/j.oceaneng.2016.05.049

Cited by 123 publications

(51 citation statements)

References 44 publications

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“…EEMD has a high ability to decompose the original signal into intrinsic mode function (IMF) components and residual components for nonstationary and nonlinear signal sequences. In the hydrological and environmental research field, EEMD has been successfully applied to predict nonlinear problems such as runoff [21][22][23], wind speed [24], wave height [25], particulate matter 2.5 (PM2.5) [26], streamflow [27,28], vegetation dynamics [29], etc. Wang et al [21] proposed an EEMD-ARIMA model for the forecasting of annual runoff time series.…”

Section: Introductionmentioning

confidence: 99%

“…The results suggested that all of the proposed hybrid algorithms are suitable for wind speed prediction. Duan et al [25] developed the improved empirical model decomposition-support vector regression (EMD-SVR) model for the short-term prediction of wave height and found that the EMD-SVR model performs better than the wavelet-decomposition-based SVR (WD-SVR) model. Ausati and Amanollahi [26] used ensemble empirical mode decomposition-general regression neural network (EEMD-GRNN), principal component regression (PCR), adaptive neuro-fuzzy inference system (ANFIS) and multiple liner regression (MLR) models to predict concentrations of particulate matter 2.5 (PM2.5) in the city of Sanandaj.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Gong

Wang

et al. 2018

Water

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Abstract:The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Gong

Wang

et al. 2018

Water

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“…An integrated numerical and ANN approach is introduced by [36] to predict waves 24 hours in advance at different buoys along the Indian Coastline. Nonlinear and non-stationary H s s are studied in [37] based on integrated Empirical Model Decomposition Support Vector Regression (EMD-SVR). Forecasting of extreme events such as hurricanes is examined by Dixit et al [38] via a Neuro Wavelet Technique (NWT).…”

Section: Ocean Characteristic Forecastingmentioning

confidence: 99%

Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks

Pirhooshyaran

Snyder

2020

Ocean Engineering

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“…Empirical Mode Decomposition (EMD) is an adaptive screening method that can screen the trend of different features existing in complex signals step by step to obtain several intrinsic mode functions (IMF) from high frequency to low frequency and the intrinsic mode functions need to meet the following two conditions [19][20][21]:…”

Section: Empirical Mode Decomposition and The Numerical Algorithmmentioning

confidence: 99%

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2018

Adv. Electr. Comp. Eng.

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A hybrid EMD-SVR model for the short-term prediction of significant wave height

Cited by 123 publications

References 44 publications

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks

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