Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series
•Development of a hybrid soft computing model to predict the Runoff.•Preprocessing the signal of the time series of input variables using the Wavelet technique.•Design an MLPNN model and train it using the PSO technique. A high-accuracy estimation of the runoff has always been an extremely relevant...
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| Veröffentlicht in: | Journal of hydrology (Amsterdam) Jg. 634; S. 131041 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.05.2024
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| ISSN: | 0022-1694, 1879-2707 |
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| Abstract | •Development of a hybrid soft computing model to predict the Runoff.•Preprocessing the signal of the time series of input variables using the Wavelet technique.•Design an MLPNN model and train it using the PSO technique.
A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science.Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDRm time series is achieved at 100. Then, the PACF(partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s. |
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| AbstractList | •Development of a hybrid soft computing model to predict the Runoff.•Preprocessing the signal of the time series of input variables using the Wavelet technique.•Design an MLPNN model and train it using the PSO technique.
A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science.Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDRm), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDRm time series is achieved at 100. Then, the PACF(partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R2 of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m3/s. Comparatively, the standalone MLP as the benchmark model results in an R2 of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m3/s. A high-accuracy estimation of the runoff has always been an extremely relevant and challenging subject in hydrology science.Therefore, in the current research, a novel hybrid decomposition-integration-optimization based model is developed to enhance the estimation precision of the runoff. The suggested predictive model is a combination of successive variational mode decomposition (SVMD) technique and Multi-Layer Perceptron neural network (MLP) model integrated with particle swarm optimization (PSO) meta-heuristic algorithm (i.e., hybrid SVMD-MLP-PSO model). To test its performance, the mean monthly runoff data recorded from Sep 1986-Aug 2017 in Dez River basin (MRDRₘ), southwest of Iran, are used. The performance of the recommended model is also matched with other different hybrid and single models including MLP-PSO, SVMD-MLP, and MLP as the benchmark model. In all models, the sequence-to-one regression module of forecasting (i.e., without using meteorological parameters recorded in the study region) is utilized. In the SVMD based hybrid models, the optimal value of compactness of mode (α) for the original MRDRₘ time series is achieved at 100. Then, the PACF(partial autocorrelation function) diagram related to the lag length from each decomposed intrinsic mode function (IMF) sub-signals sequence generated is operated to select the ideal input variables. Performance evaluation metrics prove that the hybrid SVMD-MLP-PSO model under the best predictor and meta-parameters, outperformed with an R² of 0.89, modified 2012 version of Kling-Gupta efficiency (KGEʹ) of 0.83, volumetric efficiency (VE) of 0.91, Nash–Sutcliffe efficiency (NSE) of 0.88, and RMSE of 13.91 m³/s. Comparatively, the standalone MLP as the benchmark model results in an R² of 0.24, VE of 0.33, KGEʹ of 0.2, NSE of 0.29, and RMSE of 153.39 m³/s. |
| ArticleNumber | 131041 |
| Author | Gharehbaghi, Amin Parsaie, Abbas Chadee, Aaron Anil Nou, Mohammad Rashki Ghale Ghasemlounia, Redvan Haghiabi, AmirHamzeh |
| Author_xml | – sequence: 1 givenname: Abbas surname: Parsaie fullname: Parsaie, Abbas email: Parsaie@scu.ac.ir organization: College of Water And Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran – sequence: 2 givenname: Redvan orcidid: 0000-0003-1796-4562 surname: Ghasemlounia fullname: Ghasemlounia, Redvan email: redvan.ghasemlounia@gedik.edu.tr organization: Dept. of Civil Engineering, Istanbul Gedik Univ., Postal Code: 34876, Istanbul, Turkey – sequence: 3 givenname: Amin orcidid: 0000-0002-2898-3681 surname: Gharehbaghi fullname: Gharehbaghi, Amin email: amin.gharehbaghi@hku.edu.tr organization: Dept. of Civil Engineering, Hasan Kalyoncu University, Postal Code: 27110, Şahinbey, Gaziantep, Turkey – sequence: 4 givenname: AmirHamzeh orcidid: 0000-0001-9512-0360 surname: Haghiabi fullname: Haghiabi, AmirHamzeh email: haghiabi.a@lu.ac.ir organization: Water Engineering Department, Lorestan University, Khorramabad, Iran – sequence: 5 givenname: Aaron Anil surname: Chadee fullname: Chadee, Aaron Anil email: aaron.chadee@sta.uwi.edu organization: Department of Civil and Environmental Engineering, University of the West Indies, St. Augustine Campus, St. Augustine, Trinidad, Trinidad and Tobago – sequence: 6 givenname: Mohammad Rashki Ghale surname: Nou fullname: Nou, Mohammad Rashki Ghale organization: Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran |
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| Title | Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series |
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