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Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks

  • Xinxin He
  • Jungang LuoEmail author
  • Ganggang Zuo
  • Jiancang Xie
Article
  • 45 Downloads

Abstract

Accurate and reliable runoff forecasting plays an increasingly important role in the optimal management of water resources. To improve the prediction accuracy, a hybrid model based on variational mode decomposition (VMD) and deep neural networks (DNN), referred to as VMD-DNN, is proposed to perform daily runoff forecasting. First, VMD is applied to decompose the original runoff series into multiple intrinsic mode functions (IMFs), each with a relatively local frequency range. Second, predicted models of decomposed IMFs are established by learning the deep feature values of the DNN. Finally, the ensemble forecasting result is formulated by summing the prediction sub-results of the modelled IMFs. The proposed model is demonstrated using daily runoff series data from the Zhangjiashan Hydrological Station in Jing River, China. To fully illustrate the feasibility and superiority of this approach, the VMD-DNN hybrid model was compared with EMD-DNN, EEMD-DNN, and multi-scale feature extraction -based VMD-DNN, EMD-DNN and EEMD-DNN. The results reveal that the proposed hybrid VMD-DNN model produces the best performance based on the Nash-Sutcliffe efficiency (NSE = 0.95), root mean square error (RMSE = 9.92) and mean absolute error (MAE = 3.82) values. Thus the proposed hybrid VMD-DNN model is a promising new method for daily runoff forecasting.

Keywords

Daily runoff forecasting Hybrid model Variational mode decomposition Deep neural networks 

Notes

Acknowledgments

This work was supported by the National Key R&D Program of China under Grant No. 2016YFC0401409 and the National Natural Science Foundation of China under Grant Nos. 51679186 and 51679188.

Compliance with Ethical Standards

Conflict of Interest

The authors declare no conflicts of interest.

References

  1. Ali M, Khan A, Rehman NU (2018) Hybrid multiscale wind speed forecasting based on variational mode decomposition. Int Trans Electr Energ Syst 28(1):e2466CrossRefGoogle Scholar
  2. Bai Y, Chen ZQ, Xie JJ, Li C (2016) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193–206CrossRefGoogle Scholar
  3. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828CrossRefGoogle Scholar
  4. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Proces Syst 19:153–160Google Scholar
  5. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time Series Analysis: Forecasting and Control. John Wiley & SonsGoogle Scholar
  6. Chellali F, Khellaf A, Belouchrani A (2010) Wavelet spectral analysis of the temperature and wind speed data at Adrar. Algeria Renew Energ 35(6):1214–1219CrossRefGoogle Scholar
  7. Cheng CT, Niu WJ, Feng ZK, Shen JJ, Chau KW (2015) Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water 7(8):4232–4246CrossRefGoogle Scholar
  8. Cho KH, Ilin A, Raiko T (2011) Improved learning of Gaussian-Bernoulli restricted Boltzmann machines. Lect Notes Comput Sci 6791:10–17CrossRefGoogle Scholar
  9. Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2010) Deep big simple neural nets excel on handwritten digit recognition. Neural Comput 22(12):3207–3220CrossRefGoogle Scholar
  10. Citakoglu H, Cobaner M, Haktanir T, Kisi O (2014) Estimation of long-term monthly mean reference evapotranspiration in Turkey. Water Resour Manag 28(1):99–113CrossRefGoogle Scholar
  11. Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. Proceedings of the 25th international conference on. Mach Learn:160–167Google Scholar
  12. Dabuechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36:6–7Google Scholar
  13. Di CL, Yang XH, Wang XC (2014) A four-stage hybrid model for hydrological time series forecasting. PLoS One 9(8):e104663CrossRefGoogle Scholar
  14. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544CrossRefGoogle Scholar
  15. He Y, Wang F, Mu X, Yan H, Zhao G (2015) An assessment of human versus climatic impacts on Jing River Basin, Loess Plateau, China. Advances in Meteorology. Article ID 478739Google Scholar
  16. Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Sigrurl Processing Magazine 29(6):82–97CrossRefGoogle Scholar
  17. Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800CrossRefGoogle Scholar
  18. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554CrossRefGoogle Scholar
  19. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng QA, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995CrossRefGoogle Scholar
  20. Huang SZ, Chang JX, Huang Q, Chen YT (2014) Monthly streamflow prediction using modified EMD-based support vector machine. J Hydrol 511:764–775CrossRefGoogle Scholar
  21. Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25:783–792CrossRefGoogle Scholar
  22. Koza JR (1992) Genetic Programming: On the programming of computers by means of natural selection. MIT Press 33:69–73Google Scholar
  23. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90CrossRefGoogle Scholar
  24. Lahmiri S (2015) Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis. Phys A, Stat Mech Appl 437:130–138CrossRefGoogle Scholar
  25. Lahmiri S, Boukadoum M (2015) Physiological signal denoising with variational mode decomposition and weighted reconstruction after DWT thresholding. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal pp 806–809Google Scholar
  26. Li C, Bai Y, Zeng B (2016) Deep feature learning architectures for daily reservoir inflow forecasting. Water Resour Manag 30(14):5145–5161CrossRefGoogle Scholar
  27. Lin GF, Chen GR, Huang PY, Chou YC (2009) Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. J Hydrol 372(1–4):17–29CrossRefGoogle Scholar
  28. Liu H, Mi XW, Li YF (2018) Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis. LSTM network and ELM Energy Convers Manage 159:54–64CrossRefGoogle Scholar
  29. Moghaddamnia A, Ghafari M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial nctworlcs and adaptive ncuro-fuzzy inference system techniques. Adv Water Resour 32:88–97CrossRefGoogle Scholar
  30. Mohamed AR, Dahl GE, Hinton G (2012) Acoustic modeling using deep belief networks. IEEE Trans on Audio Speech Language Process 20(1):12–22CrossRefGoogle Scholar
  31. 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. Renew Energy 118:180–212CrossRefGoogle Scholar
  32. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1):52–66CrossRefGoogle Scholar
  33. Okkan U, Serbes ZA (2012) Rainfall-runoff modeling using least squares support vector machines. Environmetrics 23(6):549–564CrossRefGoogle Scholar
  34. Ran DC, Liu B, Wang H, Luo QH, Ma Y (2006) Soil and water conservation measures and their benefits in runoff and sediment reductions of typical tributary in the middle of Yellow River. The Yellow River Water Conservancy Press, Zhengzhou, ChinaGoogle Scholar
  35. Sattari MT, Yurekli K, Pal M (2012) Performance evaluation of artificial neural network approaches in forecasting reservoir inflow. Appl Math Model 36(6):2649–2657CrossRefGoogle Scholar
  36. Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239(1):132–147CrossRefGoogle Scholar
  37. Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3):294–306CrossRefGoogle Scholar
  38. Wang WC, Xu DM, Chau KW, Chen SY (2013) Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD. J Hydroinf 15(4):1377–1390CrossRefGoogle Scholar
  39. Wang ZY, Qiu J, Li FF (2018) Hybrid models combining EMD/EEMD and ARIMA for Long-term streamflow forecasting. Water 10(7):853CrossRefGoogle Scholar
  40. Wu ZH, Huang NE (2009) Ensemble empirical mode decompostion: a noise-assisted data analysis method. Adv Adaptive Data Anal 1(1):1–41CrossRefGoogle Scholar
  41. Zhang J, Cheng CT, Liao SL, Wu XY, Shen JJ (2009) Daily reservoir inflow forecasting combining QPF into ANNs model. Hydro Earth Syst Sci 6:121–150CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Xinxin He
    • 1
  • Jungang Luo
    • 1
    Email author
  • Ganggang Zuo
    • 1
  • Jiancang Xie
    • 1
  1. 1.State Key Laboratory of Eco-hydraulics in Northwest Arid RegionXi’an University of TechnologyXi’anChina

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