Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction

Jiang, Yan; Bao, Xin; Hao, Shaonan; Zhao, Hongtao; Li, Xuyong; Wu, Xianing

doi:10.1007/s11269-020-02631-3

Cited by 39 publications

(19 citation statements)

References 42 publications

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“…where n and r present the gradient and width of the boundary of an exponential function. Equations ( 8)- (10) e PSR is a basic method for chaotic time series analysis [34]. For a time series x � {x i | i � 1, 2, .…”

Section: Complexitymentioning

confidence: 99%

A Novel Hybrid Model for Short‐Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO‐ELM

Xia¹,

Wang²

2022

Complexity

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Accurate wind speed forecasting is an effective way to improve the safety and stability of power grid. A novel hybrid model based on twice decomposition, phase space reconstruction (PSR), and an improved multiverse optimizer-extreme learning machine (IMVO-ELM) is proposed to enhance the performance of short-term wind speed forecasting in this paper. In consideration of the nonstationarity of the wind speed signal, a twice decomposition based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), fuzzy entropy, and variational mode decomposition (VMD) is proposed to reduce the nonstationarity of the original signal firstly. Then the PSR based on C-C method is employed to reconstitute the decomposed signal as the input of the prediction model. Lastly, an improved multiverse optimizer is proposed to improve the stability and efficiency of ELM which is used as prediction model. Furthermore, two experiments are designed to verify the performance of the proposed method; the results indicate that (1) the wind speed forecasting with twice decomposition of original wind speed signal is better than other once-decomposition methods and much better than forecasting without decomposition; (2) the C-C-PSR method can determine the input dimension of ELM and improve the prediction accuracy of ELM; (3) the IMVO has improved the stability of ELM, and the optimization efficiency is better than other comparison optimization methods. The results show that the proposed hybrid approach is a useful tool for short-term wind speed forecasting.

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Section: Complexitymentioning

confidence: 99%

A Novel Hybrid Model for Short‐Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO‐ELM

Xia¹,

Wang²

2022

Complexity

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“…At present, the identification of chaotic characteristics of time series is mainly based on phase space reconstruction, which can obtain more hidden information by recovering the chaotic attractor in the so-called high-dimensional phase space. The Lyapunov exponent, Correlation dimension and Kolmogorov entropy of the singular attractor are calculated to correctly distinguish the chaotic system from the random system [ 20 ]. When the correlation dimension D 2 exists at a certain value, the maximum Lyapunov exponent λ max is greater than 0 and the Kolmogorov entropy K 2 is a finite positive value, it can be judged that the time series has chaotic characteristics.…”

Section: Identification Of Chaotic Characteristicsmentioning

confidence: 99%

Dam deformation forecasting using SVM-DEGWO algorithm based on phase space reconstruction

Pan

Liu

et al. 2022

PLoS ONE

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A hybrid model integrating chaos theory, support vector machine (SVM) and the difference evolution grey wolf optimization (DEGWO) algorithm is developed to analyze and predict dam deformation. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method and the kolmogorov entropy method. Secondly, the hybrid model is established for dam deformation forecasting. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM, and the GWO algorithm is improved to realize the optimization of SVM parameters. Prior to this, the effectiveness of DEGWO algorithm based on the fusion of the difference evolution (DE) and GWO algorithm has been verified by 15 sets of test functions in CEC 2005. Finally, take the actual monitoring displacement of Jinping I super-high arch dam as examples. The engineering application examples show that the PSR-SVM-DEGWO model established performs better in terms of fitting and prediction accuracy compared with existing models.

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Section: Identification Of Chaotic Characteristicsmentioning

confidence: 99%

A Deformation Prediction Model of High Arch Dams in the Initial Operation Period Based on PSR-SVM-IGWO

Pan

Liu

et al. 2021

Mathematical Problems in Engineering

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The deformation prediction of the dam in the initial stage of operation is very important for the safety of high dams. A hybrid model integrating chaos theory, support vector machine (SVM), and an improved Grey Wolf Optimization (IGWO) algorithm is developed for deformation prediction of dam in the initial operation period. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method, and the Kolmogorov entropy method. Secondly, the SVM-IGWO model based on phase space reconstruction (PSR) is established for deformation forecasting of the dam in the initial operation period. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM and the GWO algorithm is improved to realize the optimization of SVM parameters. Finally, take the actual monitoring displacement of Xiluodu super-high arch dam as an example. The engineering application example shows that, compared with the existing models, the prediction accuracy of the PSR-SVM-IGWO model established in this paper is improved.

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Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction

Cited by 39 publications

References 42 publications

A Novel Hybrid Model for Short‐Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO‐ELM

A Novel Hybrid Model for Short‐Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO‐ELM

Dam deformation forecasting using SVM-DEGWO algorithm based on phase space reconstruction

A Deformation Prediction Model of High Arch Dams in the Initial Operation Period Based on PSR-SVM-IGWO

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