Skip to main content
SpringerLink
Log in

Monthly discharge forecasting using wavelet neural networks with extreme learning machine

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Accurate and reliable hydrological forecasting is essential for water resource management. Feedforward neural networks can provide satisfactory forecast results in most cases, but traditional gradient-based training algorithms are usually time-consuming and may easily converge to local minimum. Hence, how to obtain more appropriate parameters for feedforward neural networks with more precise prediction within shorter time has been a challenging task. Extreme learning machine (ELM), a new training algorithm for single-hidden layer feedforward neural networks (SLFNs), has been proposed to avoid these disadvantages. In this study, a conjunction model of wavelet neural networks with ELM (WNN-ELM) is proposed for 1-month ahead discharge forecasting. The à trous wavelet transform is used to decompose the original discharge time series into several sub-series. The sub-series are then used as inputs for SLFNs coupled with ELM algorithm (SLFNs-ELM); the output is the next step observed discharge. For comparison, the SLFNs-ELM and support vector machine (SVM) are also employed. Monthly discharge time series data from two reservoirs in southwestern China are derived for validating the models. In addition, four quantitative standard statistical performance evaluation measures are utilized to evaluate the model performance. The results indicate that the SLFNs-ELM performs slightly better than the SVM for peak discharge estimation, and the proposed model WNN-ELM provides more accurate forecast precision than SLFNs-ELM and SVM.

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

Similar content being viewed by others

References

  1. Lin J Y, Cheng C T, Chau K W. Using support vector machines for long-term discharge prediction. Hydrolog Sci J, 2006, 51: 599–612

    Article  Google Scholar 

  2. Xu W, Peng Y, Wang B. Evaluation of optimization operation models for cascaded hydropower reservoirs to utilize medium range forecasting inflow. Sci China Tech Sci, 2013, 56: 2540–2552

    Article  Google Scholar 

  3. Men B H, Liu C M, Lin C K. A new criterion for defining the breakpoint of the wetted perimeter-discharge curve and its application to estimating minimum instream flow requirements. Sci China Tech Sci, 2012, 55: 2686–2693

    Article  Google Scholar 

  4. Chau K W, Wu C L, Li Y S. Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng, 2005, 10: 485–491

    Article  Google Scholar 

  5. Cheng C T, Zhao M Y, Chau K W, et al. Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure. J Hydrol, 2006, 316: 129–140

    Article  Google Scholar 

  6. Tang Y H, Zhang B D, Wu J J, et al. Parallel architecture and optimization for discrete-event simulation of spike neural networks. Sci China Tech Sci, 2013, 56: 509–517

    Article  Google Scholar 

  7. Hu Q F, Yang D W, Wang Y T, et al. Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin. Sci China Tech Sci, 2013, 56: 853–865

    Article  Google Scholar 

  8. Bao W M, Zhang X Q, Zhao L P. Parameter estimation method based on parameter function surface. Sci China Tech Sci, 2013, 56: 1485–1498

    Article  Google Scholar 

  9. Ran Q H, Su D Y, Fu X D, et al. A physics-based hydro-geomorphologic simulation utilizing cluster parallel computing. Sci China Tech Sci, 2013, 56: 1883–1895

    Article  Google Scholar 

  10. Wang H, Gao X, Qian L, et al. Uncertainty analysis of hydrological processes based on ARMA-GARCH model. Sci China Tech Sci, 2012, 55: 2321–2331

    Article  Google Scholar 

  11. Shi Y, Zhou H. Research on monthly flow uncertain reasoning model based on cloud theory. Sci China Tech Sci, 2010, 53: 2408–2413

    Article  Google Scholar 

  12. Chen W, Chau K W. Intelligent manipulation and calibration of parameters for hydrological models. Int J Environ Pollut, 2006, 28: 432–447

    Article  Google Scholar 

  13. Yoon H, Jun S C, Hyun Y, et al. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol, 2011, 396: 128–138

    Article  Google Scholar 

  14. Li H Y, Zhang Y Q, Wang B D. Separating impacts of vegetation change and climate variability on streamflow using hydrological models together with vegetation data. Sci China Tech Sci, 2012, 55: 1964–1972

    Article  MathSciNet  Google Scholar 

  15. Zhang H L, Li D X, Wang X K, et al. Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC. Sci China Tech Sci, 2012, 55: 2617–2624

    Article  MathSciNet  Google Scholar 

  16. Lu F, Wang H, Yan D H, et al. Application of profile likelihood function to the uncertainty analysis of hydrometeorological extreme inference. Sci China Tech Sci, 2013, 56: 3151–3160

    Article  Google Scholar 

  17. Zhang Z, Lu W X, Chu H B, et al. Uncertainty analysis of hydrological model parameters based on the bootstrap method: A case study of the SWAT model applied to the Dongliao River Watershed, Jilin Province, Northeastern China. Sci China Tech Sci, 2014, 57: 219–229

    Article  Google Scholar 

  18. Solomatine D P, Dulal K N. Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrolog Sci J, 2003, 48: 399–411

    Article  Google Scholar 

  19. ASCE-Task-Committee. Artificial neural networks in hydrology-II: Hydrological applications. J Hydrol Eng, 2000, 5: 124–137

    Article  Google Scholar 

  20. Wu C L, Chau K W. Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol, 2011, 399: 394–409

    Article  Google Scholar 

  21. Mutlu E, Chaubey I, Hexmoor H, et al. Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process, 2008, 22: 5097–5106

    Article  Google Scholar 

  22. Muttil N, Chau K W. Neural network and genetic programming for modelling coastal algal blooms. Int J Environ Pollut, 2006, 28: 223–238

    Article  Google Scholar 

  23. Taormina R, Chau K W, Sethi R. Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intel, 2012, 25: 1670–1676

    Article  Google Scholar 

  24. Shamseldin A Y. Artificial neural network model for river flow forecasting in a developing country. J Hydroinform, 2010, 12: 22–35

    Article  Google Scholar 

  25. Hsu K L, Gupta H V, Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour Res, 1995, 31: 2517–2530

    Article  Google Scholar 

  26. Wang W C, Chau K W, Cheng C T, et al. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol, 2009, 374: 294–306

    Article  Google Scholar 

  27. Wang W, Van Gelder P, Vrijling J K, et al. Forecasting daily streamflow using hybrid ANN models. J Hydrol, 2006, 324: 383–399

    Article  Google Scholar 

  28. Garbrecht J D. Comparison of three alternative ANN designs for monthly rainfall-runoff simulation. J Hydraul Eng, 2006, 11: 502–505

    Article  Google Scholar 

  29. Wu C L, Chau K W, Li Y S. Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res, 2009, 45: W08432, doi:08410.01029/02007WR0067-37

    Article  Google Scholar 

  30. Chau K W, Wu C L. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform, 2010, 12: 458–473

    Article  Google Scholar 

  31. Xiu C B, Guo F H. Wind speed prediction by chaotic operator network based on Kalman Filter. Sci China Tech Sci, 2013, 56: 1169–1176

    Article  Google Scholar 

  32. Li R Y, Lebby G L, Si H. A modified approach for constructing the self-organized layer in a multilayer feedforward neural network. Inform Sci, 1997, 98: 69–81

    Article  Google Scholar 

  33. Hamzacebi C. Improving artificial neural networks’ performance in seasonal time series forecasting. Inform Sci, 2008, 178: 4550–4559

    Article  Google Scholar 

  34. Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70: 489–501

    Article  Google Scholar 

  35. Avci E, Coteli R. A new automatic target recognition system based on wavelet extreme learning machine. Expert Syst Appl, 2012, 39: 12340–12348

    Article  Google Scholar 

  36. Malathi V, Marimuthu N S, Baskar S. Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. Neurocomputing, 2010, 73: 2160–2167

    Article  Google Scholar 

  37. Wong W K, Guo Z X. A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int J Prod Econ, 2010, 128: 614–624

    Article  Google Scholar 

  38. Siqueira H, Boccato L, Attux R, et al. Echo state networks and extreme learning machines: a comparative study on seasonal streamflow series prediction. In: 19th International Conference on Neural Information Processing. Berlin: Springer Verlag, 2012, 491–500

    Chapter  Google Scholar 

  39. Huang M, Tian Y. A novel visual modeling system for time series forecast: application to the domain of hydrology. J Hydroinform, 2013, 15: 21–37

    Article  MathSciNet  Google Scholar 

  40. Kisi O. Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey. Hydrol Process, 2009, 23: 2081–2092

    Article  Google Scholar 

  41. Bodyanskiy Y, Vynokurova O. Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification. Inform Sci, 2013, 220: 170–179

    Article  Google Scholar 

  42. Wang W S, Ding J. Wavelet network model and its application to the prediction of hydrology. Nat Sci, 2003, 1: 67–71

    Google Scholar 

  43. Kim T W, Valdes J B. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng, 2003, 8: 319–328

    Article  Google Scholar 

  44. Partal T, Cigizoglu H K. Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J Hydrol, 2008, 358: 317–331

    Article  Google Scholar 

  45. Rajaee T, Nourani V, Zounemat-Kermani M, et al. River suspended sediment load prediction: application of ANN and wavelet conjunction model. J Hydrol Eng, 2011, 16: 613–627

    Article  Google Scholar 

  46. Partal T, Cigizoglu H K. Prediction of daily precipitation using wavelet-neural networks. Hydrolog Sci J, 2009, 54: 234–246

    Article  Google Scholar 

  47. Wang W S, Jin J L, Li Y Q. Prediction of inflow at Three Gorges Dam in Yangtze River with wavelet network model. Water Resour Manag, 2009, 23: 2791–2803

    Article  Google Scholar 

  48. Nourani V, Komasi M, Mano A. A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour Manag, 2009, 23: 2877–2894

    Article  Google Scholar 

  49. Partal T. Modelling evapotranspiration using discrete wavelet transform and neural networks. Hydrol Process, 2009, 23: 3545–3555

    Article  Google Scholar 

  50. Anctil F, Tape D G. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J Environ Eng Sci, 2004, 3: S121–S128

    Article  Google Scholar 

  51. Kisi O. Stream flow forecasting using neuro-wavelet technique. Hydrol Process, 2008, 22: 4142–4152

    Article  Google Scholar 

  52. Nourani V, Alami M T, Aminfar M H. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intel, 2009, 22: 466–472

    Article  Google Scholar 

  53. Kisi O. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng, 2009, 14: 773–782

    Article  Google Scholar 

  54. Adamowski J, Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol, 2010, 390: 85–91

    Article  Google Scholar 

  55. Pramanik N, Panda R K, Singh A. Daily river flow forecasting using wavelet ANN hybrid models. J Hydroinform, 2011, 13: 49–63

    Article  Google Scholar 

  56. Adamowski J, Chan H F. A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol, 2011, 407: 28–40

    Article  Google Scholar 

  57. Wei S, Yang H, Song J, et al. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrolog Sci J, 2013, 58: 374–389

    Article  Google Scholar 

  58. Wang Y, Wang H, Lei X, et al. Flood simulation using parallel genetic algorithm integrated wavelet neural networks. Neurocomputing, 2011, 74: 2734–2744

    Article  Google Scholar 

  59. Cimen M, Kisi O. Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol, 2009, 378: 253–262

    Article  Google Scholar 

  60. Nourani V, Komasi M, Alami M T. Hybrid wavelet-genetic programming approach to optimize ANN modeling of rainfall-runoff process. J Hydrol Eng, 2012, 17: 724–741

    Article  Google Scholar 

  61. Shensa M J. The discrete wavelet transform: Wedding the a trous and Mallat algorithms. IEEE T Signal Proces, 1992, 40: 2464–2482

    Article  MATH  Google Scholar 

  62. Maheswaran R, Khosa R. Comparative study of different wavelets for hydrologic forecasting. Computat Geosci, 2012, 46: 284–295

    Article  Google Scholar 

  63. Modarres R. Multi-criteria validation of artificial neural network rainfall-runoff modeling. Hydrol Earth Syst Sci, 2009, 13: 411–421

    Article  Google Scholar 

  64. Nash J E, Sutcliffe J V. River flow forecasting through conceptual models part I-A discussion of principles. J Hydrol, 1970, 10: 282–290

    Article  Google Scholar 

  65. Shamseldin A Y. Application of a neural network technique to rainfall-runoff modelling. J Hydrol, 1997, 199: 272–294

    Article  Google Scholar 

  66. Hu T S, Lam K C, Ng S T. River flow time series prediction with a range-dependent neural network. Hydrolog Sci J, 2001, 46: 729–745

    Article  Google Scholar 

  67. Kisi O. Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng, 2007, 12: 532–539

    Article  Google Scholar 

  68. Sudheer K P, Gosain A K, Ramasastri K S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process, 2002, 16: 1325–1330

    Article  Google Scholar 

  69. Carpenter W C, Barthelemy J F. Common misconceptions about neural networks as approximators. J Comput Civil Eng, 1994, 8: 345–358

    Article  Google Scholar 

  70. ASCE-Task-Committee. Artificial neural networks in hydrology-I: Preliminary concepts. J Hydrol Eng, 2000, 5: 115–123

    Article  Google Scholar 

  71. Zhu Q Y, Qin A K, Suganthan P N, et al. Evolutionary extreme learning machine. Pattern Recognit, 2005, 38: 1759–1763

    Article  MATH  Google Scholar 

  72. Vapnik V. The Nature of Statistical Learning Theory. New York: Springer Verlag, 1995

    Book  MATH  Google Scholar 

  73. Trafalis T B, Ince H. Support vector machine for regression and applications to financial forecasting. In: IEEE-INNS-ENNS International Conference on Neural Networks. Los Angeles: IEEE Computer Society, 2000. 348–353

    Google Scholar 

  74. Dibike Y, Velickov S, Solomatine D, et al. Model induction with support vector machines: introduction and applications. J Comput Civil Eng, 2001, 15: 208–216

    Article  Google Scholar 

  75. Pai P F. System reliability forecasting by support vector machines with genetic algorithms. Math Comput Model, 2006, 43: 262–274

    Article  MathSciNet  MATH  Google Scholar 

  76. Saini L M, Aggarwal S K, Kumar A. Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market. IEE Gener Transm D, 2010, 4: 36–49

    Article  Google Scholar 

  77. Duan Q Y, Sorooshian S, Gupta V K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol, 1994, 158: 265–284

    Article  Google Scholar 

  78. Duan Q Y, Sorooshian S, Gupta V K. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res, 1992, 28: 1015–1031

    Article  Google Scholar 

  79. Maheswaran R, Khosa R. Wavelet-Volterra coupled model for monthly stream flow forecasting. J Hydrol, 2012, 450: 320–33

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian, 116024, China

    BaoJian Li & ChunTian Cheng

Authors
  1. BaoJian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ChunTian Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ChunTian Cheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, B., Cheng, C. Monthly discharge forecasting using wavelet neural networks with extreme learning machine. Sci. China Technol. Sci. 57, 2441–2452 (2014). https://doi.org/10.1007/s11431-014-5712-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-014-5712-0

Keywords

Search

Navigation