Skip to main content
SpringerLink
Log in

A Hybrid Model for Runoff Prediction Using Variational Mode Decomposition and Artificial Neural Network

  • WATER RESOURCES AND THE REGIME OF WATER BODIES
  • Published:
Water Resources Aims and scope Submit manuscript

Abstract

Hydrological runoff prediction in a reliable and precise manner contributes significantly to the optimal management of hydropower resources. Considering the importance of runoff prediction, this study proposed a hybrid model, namely VBH (VMD-BP), coupling variational mode decomposition (VMD) technique, and backpropagation (BP) based artificial neural network (ANN), to predict the monthly runoff of Fentang reservoir, China. Two hybrid models, including ensemble empirical mode decomposition-BP (EEMD-BP) and empirical mode decomposition-BP (EMD-BP), and a standalone BP model, were also developed for comparative analysis. The VBH model performed better compared to the EEMD-BP model in reducing mean absolute error (MAE) by 40.263%, root mean square error (RMSE) by 33.634%, and mean absolute percentage error (MAPE) by 52.906%. The improved results for the VBH model compared to the EMD-BP model included 103.716, 82.266, and 158.303% reductions in MAE, RMSE, and MAPE, respectively. The error reductions by the VBH model compared to the BP model were 113.848% for MAE, 122.022% for RMSE, and 143.026% for MAPE. The results highlighted that the proposed model was superior to the hybrid and standalone counterparts for the hydrological runoff prediction. Water resources designers and planners for future planning and management of hydrological assets can exploit the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. Artificial neural networks in hydrology. I: Preliminary concepts, J. Hydrol. Eng., 2000, vol. 5, pp. 115–123.

  2. Artificial neural networks in hydrology. II: Hydrologic applications, J. Hydrol. Eng., 2000, vol. 5, pp. 124–137.

  3. Adamowski, J., Chan, H.F., Prasher, S.O., and Sharda, V.N., Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data, J. Hydroinform., 2012, vol. 14, pp. 731–744.

    Article  Google Scholar 

  4. Barzegar, R., Adamowski, J., and Moghaddam, A.A., Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran, Stoch. Env. Res. Risk A., 2016, vol. 30, pp. 1797–1819.

    Article  Google Scholar 

  5. Burger, C.M., Kolditz, O., Fowler, H.J., and Blenkinsop, S., Future climate scenarios and rainfall-runoff modelling in the Upper Gallego catchment (Spain), Environ. Pollut., 2007, vol. 148, pp. 842–854.

    Article  Google Scholar 

  6. Cannas, B., Fanni, A., Sias, G., Tronci, S., and Zedda, M., River flow forecasting using neural networks and wavelet analysis, Geophys. Res. Abstr, 2005, vol. 7, p. 08651.

    Google Scholar 

  7. Chandwani, V., Vyas, S.K., Agrawal, V., and Sharma, G., Soft computing approach for rainfall-runoff modelling: a review, Aquat. Procedia, 2015, vol. 4, pp. 1054–1061.

    Article  Google Scholar 

  8. Chen, Y. and Xu, Z., Plausible impact of global climate change on water resources in the Tarim River Basin, Sci. China Ser. D-Earth Sci., 2005, vol. 48, pp. 65–73.

    Article  Google Scholar 

  9. Chu, H., Wei, J., Li, J., Qiao, Z., and Cao, J., Improved medium-and long-term runoff forecasting using a multimodel approach in the Yellow River Headwaters region based on large-scale and local-scale climate information, Water, 2017, vol. 9, p. 608.

    Article  Google Scholar 

  10. Coulibaly, P. and Baldwin, C.K., Nonstationary hydrological time series forecasting using nonlinear dynamic methods, J. Hydrol., 2005, vol. 307, pp. 164–174.

    Article  Google Scholar 

  11. De Vos, N. and Rientjes, T., Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation, Hydrol. Earth Syst. Sci., 2005, vol. 9, pp. 111–126.

    Article  Google Scholar 

  12. Dragomiretskiy, K. and Zosso, D., Variational mode decomposition, IEEE T. Signal Proces., 2013, vol. 62, pp. 531–544.

    Article  Google Scholar 

  13. Elganiny, M.A. and Eldwer, A.E., Enhancing the Forecasting of Monthly Streamflow in the Main Key Stations of the River Nile Basin, Water Resour., 2018, vol. 45, pp. 660–671.

    Article  Google Scholar 

  14. Farajzadeh, J. and Alizadeh, F., A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model, J. Hydroinform, 2018, vol. 20, pp. 246–262.

    Article  Google Scholar 

  15. Feng, Z.-K., Niu, W.-J., Cheng, C.-T., and Wu, X.-Y., Optimization of hydropower system operation by uniform dynamic programming for dimensionality reduction, Energy, 2017, vol. 134, pp. 718–730.

    Article  Google Scholar 

  16. Fortin, V., Ouarda, T., Rasmussen, P., and Bobée, B., A review of streamflow forecasting methods, Rev. Sci. Eau., 1997, vol. 10, pp. 461–487. (in French)

    Google Scholar 

  17. Guo, Z., Zhao, W., Lu, H., and Wang, J., Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model, Renew. Energ., 2012, vol. 37, pp. 241–249.

    Article  Google Scholar 

  18. Huang, N., Yuan, C., Cai, G., and Xing, E., Hybrid short term wind speed forecasting using variational mode decomposition and a weighted regularized extreme learning machine, Energies, 2016, vol. 9, p. 989.

    Article  Google Scholar 

  19. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., and Liu, H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Roy. Soc. London A, 1998, vol. 454, pp. 903–995.

    Article  Google Scholar 

  20. Jiang, Z., Sun, P., Ji, C., and Zhou, J., Credibility theory based dynamic control bound optimization for reservoir flood limited water level, J. Hydrol., 2015, vol. 529, pp. 928–939.

    Article  Google Scholar 

  21. Jianwei, E., Bao, Y., and Ye, J., Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis, Physica A, 2017, vol. 484, pp. 412–427.

    Article  Google Scholar 

  22. Karthikeyan, L. and Kumar, D.N., Predictability of nonstationary time series using wavelet and EMD based ARMA models, J. Hydrol., 2013, vol. 502, pp. 103–119.

    Article  Google Scholar 

  23. Khair, U., Fahmi, H., Al Hakim, S., and Rahim, R., Forecasting error calculation with mean absolute deviation and mean absolute percentage error, J. Phys. Conf. Ser., 2017, vol. 930, p. 012002.

    Article  Google Scholar 

  24. Kisi, O. and Kerem Cigizoglu, H., Comparison of different ANN techniques in river flow prediction, Civil Eng. Environ. Syst., 2007, vol. 24, pp. 211–231.

    Article  Google Scholar 

  25. Lahmiri, S., A variational mode decompoisition approach for analysis and forecasting of economic and financial time series, Expert Syst. Appl., 2016, vol. 55, pp. 268–273.

    Article  Google Scholar 

  26. Li, H., Zhang, Y., and Zhou, X., Predicting surface runoff from catchment to large region, Adv. Meteorol., 2015, vol. 2015.

  27. Lohani, A., Kumar, R., and Singh, R., Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques, J. Hydrol., 2012, vol. 442, pp. 23–35.

    Article  Google Scholar 

  28. Mahgoun, H., Bekka, R.E., and Felkaoui, A., Gearbox fault diagnosis using ensemble empirical mode decomposition (EEMD) and residual signal, Mech. Ind., 2012, vol. 13, pp. 33–44.

    Article  Google Scholar 

  29. Maier, H.R. and Dandy, G.C., Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ. Modell. Softw., 2000, vol. 15, pp. 101–124.

    Article  Google Scholar 

  30. Mosavi, A., Ozturk, P., and Chau, K.-w., Flood prediction using machine learning models: Literature review, Water, 2018, vol. 10, p. 1536.

    Article  Google Scholar 

  31. Napolitano, G., Serinaldi, F., and See, L., Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination, J. Hydrol., 2011, vol. 406, pp. 199–214.

    Article  Google Scholar 

  32. Niu, W.-J., Feng, Z.-K., Zeng, M., Feng, B.-F., Min, Y.-W., Cheng, C.-T., and Zhou, J.-Z., Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm, Appl. Soft Comput., 2019, vol. 82, p. 105589.

    Article  Google Scholar 

  33. Nourani, V., Kisi, Ö., and Komasi, M., Two hybrid artificial intelligence approaches for modeling rainfall–runoff process, J. Hydrol., 2011, vol. 402, pp. 41–59.

    Article  Google Scholar 

  34. Nunes, J.C., Bouaoune, Y., Delechelle, E., Niang, O., and Bunel, P., Image analysis by bidimensional empirical mode decomposition, Image Vision Comput., 2003, vol. 21, pp. 1019–1026.

    Article  Google Scholar 

  35. Sahay, R.R. and Sehgal, V., Wavelet-ANFIS models for forecasting monsoon flows: case study for the Gandak River (India), Water Resour., 2014, vol. 41, pp. 574–582.

    Article  Google Scholar 

  36. Sang, Y.-F., Wang, Z., and Liu, C., Period identification in hydrologic time series using empirical mode decomposition and maximum entropy spectral analysis, J. Hydrol., 2012, vol. 424, pp. 154–164.

    Article  Google Scholar 

  37. Shortridge, J.E., Guikema, S.D., and Zaitchik, B.F., Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds, Hydrol. Earth Syst. Sci., 2016, vol. 20, pp. 2611–2628.

    Article  Google Scholar 

  38. Sibtain, M., Li, X., Nabi, G., Azam, M.I., and Bashir, H., Development of a three-stage hybrid model by utilizing a two-stage signal decomposition methodology and machine learning approach to predict monthly runoff at Swat river basin, Pakistan, Discrete Dyn. Nat. Soc., 2020, vol. 2020, p. 7345676.

    Article  Google Scholar 

  39. Sohail, A., Watanabe, K., and Takeuchi, S., Runoff analysis for a small watershed of Tono area Japan by back propagation artificial neural network with seasonal data, Water. Resour. Manage., 2008, vol. 22, pp. 1–22.

    Article  Google Scholar 

  40. Sudheer, K., Gosain, A., and Ramasastri, K., A data-driven algorithm for constructing artificial neural network rainfall-runoff models, Hydrol. Process., 2002, vol. 16, pp. 1325–1330.

    Article  Google Scholar 

  41. Sun, G., Chen, T., Wei, Z., Sun, Y., Zang, H., and Chen, S., A carbon price forecasting model based on variational mode decomposition and spiking neural networks, Energies, 2016, vol. 9, p. 54.

    Article  Google Scholar 

  42. Tan, Q.-F., Lei, X.-H., Wang, X., Wang, H., Wen, X., Ji, Y., and Kang, A.-Q., An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach, J. Hydrol., 2018, vol. 567, pp. 767–780.

    Article  Google Scholar 

  43. Tongal, H. and Booij, M.J., Simulation and forecasting of streamflows using machine learning models coupled with base flow separation, J. Hydrol., 2018, vol. 564, pp. 266–282.

    Article  Google Scholar 

  44. Wang, W.-C., Chau, K.-W., Cheng, C.-T., and Qiu, L., A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, J. Hydrol., 2009, vol. 374, pp. 294–306.

    Article  Google Scholar 

  45. Wang, X., Yu, Q., and Yang, Y., Short-term wind speed forecasting using variational mode decomposition and support vector regression, J. Intell. Fuzzy Syst., 2018, vol. 34, pp. 3811–3820.

    Article  Google Scholar 

  46. Wang, Y. and Markert, R., Filter bank property of variational mode decomposition and its applications, Signal Process., 2016, vol. 120, pp. 509–521.

    Article  Google Scholar 

  47. Wang, Z., He, G., Du, W., Zhou, J., Han, X., Wang, J., He, H., Guo, X., Wang, J., and Kou, Y., Application of parameter optimized variational mode decomposition method in fault diagnosis of gearbox, IEEE Access, 2019, vol. 7, pp. 44871–44882.

    Article  Google Scholar 

  48. Wu, C. and Chau, K.W., Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis, J. Hydrol., 2011, vol. 399, pp. 394–409.

    Article  Google Scholar 

  49. Wu, Z. and Huang, N.E., Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adapt. Data Anal., 2009, vol. 1, pp. 1–41.

    Article  Google Scholar 

  50. Yeh, J.-R., Shieh, J.-S., and Huang, N.E., Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method, Adv. Adapt. Data Anal., 2010, vol. 2, pp. 135–156.

    Article  Google Scholar 

  51. Yildiz, S. and Degirmenci, M., Estimation of oxygen exchange during treatment sludge composting through multiple regression and artificial neural networks (estimation of oxygen exchange during composting), Int. J. Environ. Res., 2015, vol. 9, pp. 1173–1182.

    Google Scholar 

  52. Young, C.-C. and Liu, W.-C., Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model, Hydrol. Sci. J., 2015, vol. 60, pp. 2102–2116.

    Article  Google Scholar 

  53. Yu, Y., Zhang, H., and Singh, V.P., Forward prediction of runoff data in data-scarce basins with an improved ensemble empirical mode decomposition (EEMD) model, Water, 2018, vol. 10, p. 388.

    Article  Google Scholar 

  54. Zhang, H., Singh, V.P., Wang, B., and Yu, Y., CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system, J. Hydrol., 2016, vol. 540, pp. 246–256.

    Article  Google Scholar 

  55. Zhang, X., Zhang, Q., Zhang, G., Nie, Z., Gui, Z., and Que, H., A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural network based on ensemble empirical mode decomposition, Int. J. Environ. Res. Public Health, 2018, vol. 15, p. 1032.

    Article  Google Scholar 

  56. Zhao, X., Chen, X., and Huang, Q., Trend and long-range correlation characteristics analysis of runoff in upper Fenhe River basin, Water Resour., 2017, vol. 44, pp. 31–42.

    Article  Google Scholar 

  57. Zheng, J., Cheng, J., and Yang, Y., Partly ensemble empirical mode decomposition: An improved noise-assisted method for eliminating mode mixing, Signal Process., 2014, vol. 96, pp. 362–374.

    Article  Google Scholar 

Download references

ACKNOWLEDGMENTS

This study was supported by the National Natural Science Foundation of China (grant no. 51607105); and the Provincial Natural Science Foundation of HUBEI Province (grant no. 2016CFA097). This financial support is gratefully appreciated and acknowledged.

Author information

Authors and Affiliations

  1. Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, 44302, Yichang, P.R. China

    Muhammad Sibtain & Xianshan Li

  2. College of Environmental Science and Engineering, Hunan University, 410082, Changsha, P.R. China

    Hassan Bashir

  3. College of Hydraulic and Environmental Engineering, China Three Gorges University, 44302, Yichang, P.R. China

    Muhammad Imran Azam

Authors
  1. Muhammad Sibtain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianshan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Bashir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Imran Azam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Muhammad Sibtain, Xianshan Li, Hassan Bashir or Muhammad Imran Azam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muhammad Sibtain, Li, X., Bashir, H. et al. A Hybrid Model for Runoff Prediction Using Variational Mode Decomposition and Artificial Neural Network. Water Resour 48, 701–712 (2021). https://doi.org/10.1134/S0097807821050171

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0097807821050171

Keywords:

Search

Navigation