nach oben

Environmental Earth Sciences

Erschienen in:

01.01.2024 | Original Article

Monthly runoff prediction using gated recurrent unit neural network based on variational modal decomposition and optimized by whale optimization algorithm

verfasst von: Wen-chuan Wang, Bo Wang, Kwok-wing Chau, Yan-wei Zhao, Hong-fei Zang, Dong-mei Xu

Erschienen in: Environmental Earth Sciences | Ausgabe 2/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

To further increase the forecast precision of non-stationary non-linear monthly runoff series and improve the effectiveness of pretreatment of monthly runoff series, the whale optimization algorithm (WOA) is introduced to optimize the variational mode decomposition (VMD), and the WOA–VMD–GRU prediction model is constructed by coupling with the gating cycle unit (GRU) neural network. First, the variation modal decomposition is optimized by the whale optimization algorithm, to find the best decomposition modal number k and penalty factor α, then several IMF components are obtained according to VMD processing runoff sequences; finally, results are obtained by adding those of each component. Taking Manwan Hydropower, Hongjiadu Hydropower, and Changshui Hydrological Station as examples, the BP model, the GRU model, the EMD–GRU model, the CEEMDAN–GRU model, and the VMD–GRU model are compared. Four quantitative indexes were used to estimate the model performance. The results show that the proposed WOA–VMD–GRU model has the best prediction accuracy, with correlation coefficients and Nash coefficients above 0.99 and 0.97 in the prediction results of the three hydrological stations, respectively, and avoids the low efficiency of VMD decomposition parameters in manual trial computation, providing a new way for monthly runoff prediction.

Graphical Abstract

Vorheriger Artikel Effects of mine water discharge on river sediments: metal fate and behaviour, Upper Silesian Coal Basin

Nächster Artikel Spectral classification of AVIRIS NG hyperspectral data for discriminating coastal foredunes based on vegetation species: a case study from Cuddalore district of Tamil Nadu, South India

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abdoos AA (2016) A new intelligent method based on combination of VMD and ELM for short term wind power forecasting. Neurocomputing 203:111–120. https://doi.org/10.1016/j.neucom.2016.03.054CrossRef

Altan A, Karasu S, Zio E (2021) A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl Soft Comput 100:106996. https://doi.org/10.1016/j.asoc.2020.106996CrossRef

Anaraki MV, Achite M, Farzin S, Elshaboury N, Al-Ansari N, Elkhrachy I (2023a) Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin. Algeria Water 15:3576. https://doi.org/10.3390/w15203576CrossRef

Anaraki MV, Kadkhodazadeh M, Morshed-Bozorgdel A, Farzin S (2023b) Predicting rainfall response to climate change and uncertainty analysis: Introducing a novel downscaling CMIP6 models technique based on the stacking ensemble machine learning. Journal of Water and Climate Change 14:3671–3691. https://doi.org/10.2166/wcc.2023.477CrossRef

Bai T, Chang JX, Chang FJ, Huang Q, Wang YM, Chen GS (2015) Synergistic gains from the multi-objective optimal operation of cascade reservoirs in the Upper Yellow River basin. J Hydrol 523:758–767. https://doi.org/10.1016/j.jhydrol.2015.02.007CrossRef

Büyükşahin ÜÇ, Ertekin Ş (2019) Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing 361:151–163. https://doi.org/10.1016/j.neucom.2019.05.099CrossRef

Chen Y, Liu G, Huang X, Chen K, Hou J, Zhou J (2021) Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis. J Hydrol 598:125726. https://doi.org/10.1016/j.jhydrol.2020.125726CrossRef

Chen D, Zhang Y, Yao C, Lai B, Shijun L (2017) Fault diagnosis method based on variational mode decomposition and multi-scale permutation entropy. Computer Integrated Manufacturing Systems 23:2604–2612. https://doi.org/10.13196/j.cims.2017.12.005

Dragomiretskiy K, Zosso D (2014) Variational Mode Decomposition. IEEE Trans Signal Process 62:531–544. https://doi.org/10.1109/tsp.2013.2288675CrossRef

Elshorbagy A, Simonovic SP, Panu US (2000) Performance Evaluation of Artificial Neural Networks for Runoff Prediction. J Hydrol Eng 5:424–427. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:4(424)CrossRef

Farzin S, Anaraki MV, Naeimi M, Zandifar S (2022) Prediction of groundwater table and drought analysis; a new hybridization strategy based on bi-directional long short-term model and the Harris hawk optimization algorithm. Journal of Water and Climate Change 13:2233–2254. https://doi.org/10.2166/wcc.2022.066CrossRef

Feltus C (2022) Learning Algorithm Recommendation Framework for IS and CPS Security: Analysis of the RNN, LSTM, and GRU Contributions. International Journal of Systems and Software Security and Protection (IJSSSP) 13:1–23. https://doi.org/10.4018/IJSSSP.293236CrossRef

Feng Z-k, Niu W-j, Zhang R, Wang S, Cheng C-t (2019) Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. J Hydrol 576:229–238. https://doi.org/10.1016/j.jhydrol.2019.06.045CrossRef

Feng Z-k, Niu W-j, Tang Z-y, Jiang Z-q, Xu Y, Liu Y, Zhang H-r (2020) Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. J Hydrol 583:124627. https://doi.org/10.1016/j.jhydrol.2020.124627CrossRef

Fengrong BI, Xin LI, Teng MA (2018) Knock Detection Using Variational Mode Decomposition. Journal of Vibration,Measurement Diagnosis 38:903–907. https://doi.org/10.16450/j.cnki.issn.1004-6801.2018.05.004

Fosso OB, Molinas M EMD Mode Mixing Separation of Signals with Close Spectral Proximity in Smart Grids. In: IEEE PES ISGT Europe 2018 - 8th IEEE PES Innovative Smart Grid Technologies, 2018.

Gao S, Huang Y, Zhang S, Han J, Wang G, Zhang M, Lin Q (2020) Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J Hydrol 589:125188. https://doi.org/10.1016/j.jhydrol.2020.125188CrossRef

Gao Y (2012) Application of BP Neural Network to Runoff Forecasting in Karst Mountainous Area in Guizhou. Ground Water

Guo Z, Zhao W, Lu H, Wang J (2012) Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy 37:241–249. https://doi.org/10.1016/j.renene.2011.06.023CrossRef

Han L, Zhang R, Wang X, Bao A, Jing HJIRPG (2019) Multi-Step Wind Power Forecast based on VMD-LSTM. IET Renew Power Gener 13:1690–1700. https://doi.org/10.1049/iet-rpg.2018.5781CrossRef

He F, Zhou J, Feng ZK, Liu G, Yang Y (2019) A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm. Appl Energy 237:103–116. https://doi.org/10.1016/j.apenergy.2019.01.055CrossRef

He X, Luo J, Li P, Zuo G, Xie J (2020) A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting. Water Resour Manage 34:865–884. https://doi.org/10.1007/s11269-020-02483-xCrossRef

Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735CrossRef

Hu H, Zhang J, Li T (2020) A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series. Complexity 2020:1–21. https://doi.org/10.1155/2020/4064851CrossRef

Ji CM, Zhou T, Huang HT (2014) Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression. Water Resour Manage 28:2435–2451. https://doi.org/10.1007/s11269-014-0610-6CrossRef

Jin Z, He D, Lao Z, Wei Z, Yin X, Yang W (2022) Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM. Nonlinear Dyn. https://doi.org/10.1007/s11071-022-08109-8CrossRef

Kingston GB, Maier HR, Lambert MF (2005) Calibration and validation of neural networks to ensure physically plausible hydrological modeling. J Hydrol 314:158–176. https://doi.org/10.1016/j.jhydrol.2005.03.013CrossRef

Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324. https://doi.org/10.1109/5.726791CrossRef

Lei Y, He Z, Zi Y (2009) Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process 23:1327–1338. https://doi.org/10.1016/j.ymssp.2008.11.005CrossRef

Li C, Tang G, Xue X, Saeed A, Hu X (2020a) Short-Term Wind Speed Interval Prediction Based on Ensemble GRU Model. IEEE Transactions on Sustainable Energy 11:1370–1380. https://doi.org/10.1109/TSTE.2019.2926147CrossRef

Li J, Chen W, Han K, Wang Q (2020b) Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM. IEEE Access 8:166753–166767. https://doi.org/10.1109/ACCESS.2020.3023306CrossRef

Li B-J, Sun G-L, Liu Y, Wang W-C, Huang X-D (2022) Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks. Water Resour Manage 36:2095–2115. https://doi.org/10.1007/s11269-022-03133-0CrossRef

Li H, Wu X, Liu T, Chen Q (2018) Application of variational mode decomposition and improved adaptive resonance technology in bearing fault feature extraction. Journal of Vibration Engineering 31:718–726. https://doi.org/10.16385/j.cnki.issn.1004-4523.2018.04.020

Liu D et al (2021) Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model. J Hydrol 592:125801. https://doi.org/10.1016/j.jhydrol.2020.125801CrossRef

Ma Z, Zhang J, Zhang A, Ruan W (2018) Fault feature extraction of rolling bearings based on VMD-SVD joint de-noising and FSWT. Journal of Vibration Shock 37:210–217. https://doi.org/10.13465/j.cnki.jvs.2018.17.029

Meng X, Yin M, Ning L, Liu D, Xue X (2015) A threshold artificial neural network model for improving runoff prediction in a karst watershed. Environmental Earth Sciences 74:5039–5048. https://doi.org/10.1007/s12665-015-4562-9CrossRef

Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008CrossRef

Modaresi F, Araghinejad S, Ebrahimi K (2018) A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions. Water Resour Manage 32:243–258. https://doi.org/10.1007/s11269-017-1807-2CrossRef

Naik J, Dash S, Dash PK, Bisoi R (2018) Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network. Renewable Energy 118:180–212. https://doi.org/10.1016/j.renene.2017.10.111CrossRef

Niu W-j, Feng Z-k, Zeng M, Feng B-f, Min Y-w, Cheng C-t, Zhou J-z (2019) Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Appl Soft Comput 82:105589. https://doi.org/10.1016/j.asoc.2019.105589CrossRef

Qiang Z, Wang BD, He B, Yong P, Ren ML (2011) Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting. Water Resour Manage 25:2683–2703. https://doi.org/10.1007/s11269-011-9833-yCrossRef

Si H et al (2020) Analysis of resonance data in substations based on WOA- VMD-DWT. IOP Conference Series: Materials Science and Engineering 768:062050. https://doi.org/10.1088/1757-899X/768/6/062050CrossRef

Taormina R, Chau KW, Sivakumar B (2015) Neural network river forecasting through baseflow separation and binary-coded swarm optimization. J Hydrol 529:1788–1797. https://doi.org/10.1016/j.jhydrol.2015.08.008CrossRef

Torres ME, Colominas MA, Schlotthauer G, Flandrin P A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 22–27 May 2011 2011. pp 4144–4147. https://doi.org/10.1109/ICASSP.2011.5947265

Verma S, Mishra SK, Singh A, Singh PK, Verma RK (2017) An enhanced SMA based SCS-CN inspired model for watershed runoff prediction. Environmental Earth Sciences 76:736. https://doi.org/10.1007/s12665-017-7062-2CrossRef

Wang W-C, Chau K-W, Cheng C-T, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019CrossRef

Wang W-c, Chau K-w, Xu D-m, Chen X-Y (2015) Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition. Water Resour Manage 29:2655–2675. https://doi.org/10.1007/s11269-015-0962-6CrossRef

Wang W-c, Du Y-j, Chau K-w, Xu D-m, Liu C-j, Ma Q (2021) An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network. Water Resour Manage 35:4695–4726. https://doi.org/10.1007/s11269-021-02920-5CrossRef

Wang X, Zhang S, Qiao H, Liu L, Tian F (2022) Mid-long term forecasting of reservoir inflow using the coupling of time-varying filter-based empirical mode decomposition and gated recurrent unit. Environ Sci Pollut Res 29:87200–87217. https://doi.org/10.1007/s11356-022-21634-8CrossRef

Wang W, Jun MA (2005) Review on some methods for hydrological forecasting. Advances In Science and Technology of Water Resources

Wei F, Huang S, Ren K, Qiang H, Li K (2019) Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models. J Hydrol 568:534–550. https://doi.org/10.1016/j.jhydrol.2018.11.020CrossRef

Wei H, Xiong L, Zhao F, Tang G, Lane SN (2022) Large-scale spatial variability in loess landforms and their evolution, Luohe River Basin. Chinese Loess Plateau Geomorphology 415:108407. https://doi.org/10.1016/j.geomorph.2022.108407CrossRef

Wen X, Feng Q, Deo RC, Wu M, Yin Z, Yang L, Singh VP (2019) Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems. J Hydrol 570:167–184. https://doi.org/10.1016/j.jhydrol.2018.12.060CrossRef

Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal 1:1–41. https://doi.org/10.1142/s1793536909000047CrossRef

Wu Z et al (2022) Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China. J Hydrol 612:128084. https://doi.org/10.1016/j.jhydrol.2022.128084CrossRef

Xu C et al (2016) Scenario-based runoff prediction for the Kaidu River basin of the Tianshan Mountains. Northwest China Environmental Earth Sciences 75:1126. https://doi.org/10.1007/s12665-016-5930-9CrossRef

Xu B, Zhong PA, Lu Q, Zhu F, Fu J (2020) Multiobjective stochastic programming with recourses for real-time flood water conservation of a multireservoir system under uncertain forecasts. J Hydrol 590:125513. https://doi.org/10.1016/j.jhydrol.2020.125513CrossRef

Xu D-m, Wang X, Wang W-c, Chau K-w, Zang H-f (2023) Improved monthly runoff time series prediction using the SOA–SVM model based on ICEEMDAN–WD decomposition. J Hydroinf 25:943–970. https://doi.org/10.2166/hydro.2023.172CrossRef

Yaseen ZM, Kisi O, Demir VJWRM (2016) Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence. Water Resour Manage 30:4125–4151. https://doi.org/10.1007/s11269-016-1408-5CrossRef

Youngmin S, Sungwon K, Vijay S (2018) Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff. Atmosphere 9:251. https://doi.org/10.3390/atmos9070251CrossRef

Yu L, Wang S, Lai KK (2008) Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics 30:2623–2635. https://doi.org/10.1016/j.eneco.2008.05.003CrossRef

Zeng F, Ma MG, Di DR, Shi WY (2020) Separating the Impacts of Climate Change and Human Activities on Runoff: A Review of Method and Application. Water 12:2201. https://doi.org/10.3390/w12082201CrossRef

Zhao-Xia GE, Yue-Hong GU, Cao LQ, Qiang XM (2006) Influences of sea surface temperature and meteorological factors on monthly runoff of Hongjiadu Hydropower Station. Journal of Hohai University(Natural Sciences) 34:606–609

Zhou Q, Ou XK, Zhang ZM, Yang MY (2008) Spatial-Temporal Land Use Pattern Changes in Manwan Hydropower Station Reservoir of Lancang River, Yunnan, China. J Mt Sci 26:481–489

Zhou Y, Shi J, Chen H, Ding T Interval Prediction of Photovoltaic Output Based on WOA-LSTM-LSSVM Combined Model. In: 6th Asia Conference on Power and Electrical Engineering, ACPEE 2021, April 8, 2021 - April 11, 2021, Chongqing, China, 2021. Proceedings - 2021 6th Asia Conference on Power and Electrical Engineering, ACPEE 2021. Institute of Electrical and Electronics Engineers Inc., pp 514–519. https://doi.org/10.1109/ACPEE51499.2021.9436884

Zuo G, Luo J, Wang N, Lian Y, He X (2020) Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting. J Hydrol 585:124776. https://doi.org/10.1016/j.jhydrol.2020.124776CrossRef

Titel: Monthly runoff prediction using gated recurrent unit neural network based on variational modal decomposition and optimized by whale optimization algorithm
verfasst von: Wen-chuan Wang
Bo Wang
Kwok-wing Chau
Yan-wei Zhao
Hong-fei Zang
Dong-mei Xu
Publikationsdatum: 01.01.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 2/2024
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-023-11377-1

Springer Professional

Abstract

Graphical Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2024

Combining fission-track radiography and scanning electron microscopy to identify uranium host phases

Assessing groundwater vulnerability and addressing salinization in the coastal region of Ba Ria–Vung Tau province, Vietnam: an enhanced DRASTIC model approach

Introductory editorial to the topical collection: hydrogeological and geomorphological advances in karst and caves research

An experimental study on water and sand leakage disaster of gap-graded sandy soil under different grain size distributions

Influence of biomass and chemicals on kinetics of ureolysis-based carbonate biomineral precipitation

Delineation of lineaments for groundwater prospecting in hard rocks: inferences from remote sensing and geophysical data