Advertisement

Acta Geophysica

, Volume 67, Issue 4, pp 1223–1240 | Cite as

Long-term monthly streamflow forecasting in humid and semiarid regions

  • Amel Fouchal
  • Doudja Souag-GamaneEmail author
Research Article - Hydrology
  • 69 Downloads

Abstract

Long-term monthly streamflow forecasting has great importance in the water resource system planning. However, its modelling in extreme cases is difficult, especially in semiarid regions. The main purpose of this paper is to evaluate the accuracy of artificial neural networks (ANNs) and hybrid wavelet-artificial neural networks (WA-ANNs) for multi-step monthly streamflow forecasting in two different hydro-climatic regions in Northern Algeria. Different issues have been addressed, both those related to the model’s structure and those related to wavelet transform. The discrete wavelet transform has been used for the preprocessing of the input variables of the hybrid models, and the multi-step streamflow forecast was carried out by means of a recursive approach. The study demonstrated that WA-ANN models outperform the single ANN models for the two hydro-climatic regions. According to the performance criteria used, the results highlighted the ability of WA-ANN models with lagged streamflows, precipitations and evapotranspirations to forecast up to 19 months for the humid region with good accuracy [Nash–Sutcliffe criterion (Ns) equal 0.63], whereas, for the semiarid region, the introduction of evapotranspirations does not improve the model’s accuracy for long lead time (Ns less than 0.6 for all combinations used). The maximum lead time achieved, for the semiarid region, was about 13 months, with only lagged streamflows as inputs.

Keywords

Neural networks Multi-step forecasting Monthly streamflow Wavelet transform Hydro-climatic regions 

References

  1. Abda Z, Chettih M (2018) Forecasting daily flow rate-based intelligent hybrid models combining wavelet and Hilbert–Huang transforms in the mediterranean basin in northern Algeria. Acta Geophys.  https://doi.org/10.1007/s11600-018-0188-0 Google Scholar
  2. Addison PS (2002) The illustrated wavelet handbook: introduction theory and applications in science, engineering, medicine and finance. IOP Publishing LtdGoogle Scholar
  3. Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semiarid watersheds. J Hydrol 390:85–91CrossRefGoogle Scholar
  4. Akrami SA, El-Shafie A, Naseri M, Santos CAG (2014) Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy. Neural Comput Appl 25:1853–1861CrossRefGoogle Scholar
  5. Balkin SD, Ord JK (2000) Automatic neural network modeling for univariate time series. Int J Forecast 16:509–515CrossRefGoogle Scholar
  6. Baratti R, Cannas B, Fanni A, Pintus M, Sechi GM, Toreno N (2003) River flow forecast for reservoir management through neural networks. Neurocomputing 55(3):421–437CrossRefGoogle Scholar
  7. Burden F, Winkler D (2008) Bayesian regularization of neural networks. In: Livingstone DS (ed) Artificial neural networks: methods in molecular biology™, vol 458. Humana Press.  https://doi.org/10.1007/978-1-60327-101-1_3
  8. Chiew FHS, McMahon TA (2002) Global ENSO-streamflow teleconnection, streamflow forecasting and interannual variability. Hydrol Sci J 47(3):505–522CrossRefGoogle Scholar
  9. Danadeh Mehr A, Kahya E, Şahin A, Nazemosadat MJ (2014) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12:2191–2200CrossRefGoogle Scholar
  10. Danandeh Mehr A, Kahya E, Şahin A, Nazemosadat MJ (2015) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12(7):2191–2200CrossRefGoogle Scholar
  11. Daubechies I (1992) Ten lectures on wavelets. In: CSBM-NSF series on applied mathematics, vol 61. SIAM PublicationGoogle Scholar
  12. Djerbouai S, Souag-Gamane D (2016) Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of Algerois Basin in North Algeria. Water Resour Res.  https://doi.org/10.1007/s11269-016-1298-6 Google Scholar
  13. Dunne T (1983) Relation of field studies and modeling in the prediction of storm runoff. J Hydrol 65:25–48CrossRefGoogle Scholar
  14. Foresee D, Hagan MT (1997) Gauss-Newton approximation to Bayesian learning. In: Proceedings of the 1997 international joint conference on neural networks, vol 3, pp 1930–1935Google Scholar
  15. Geman S, Bienenstock E, Dourast R (1992) Neural networks and the bias/variance dilemma. Neural Comput 04:1–58CrossRefGoogle Scholar
  16. Hadi SJ, Tombul M (2018a) Streamflow forecasting using four wavelet transformation combinations approaches with data-driven models: a comparative study. Water Resour Manag.  https://doi.org/10.1007/s11269-018-2077-3 Google Scholar
  17. Hadi SJ, Tombul M (2018b) Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination. J Hydrol 561:674–687CrossRefGoogle Scholar
  18. He Z, Zhang Y, Guo Q, Zhao X (2014) Comparative study of artificial neural networks and wavelet artificial neural networks for groundwater depth data forecasting with various curve fractal dimensions. Water Resour Manag 28:5297–5317.  https://doi.org/10.1007/s11269-014-0802-0 CrossRefGoogle Scholar
  19. Ji Y, Hao J, Reyhani N, Lendasse A (2005) Direct and recursive prediction of time series using mutual information selection neural network. Lect Notes Comput Sci 3512:1010–1017CrossRefGoogle Scholar
  20. Karran DJ, Morin E, Adamowski J (2014) Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes. J Hydroinform 16:671–689CrossRefGoogle Scholar
  21. Kayri M (2016) Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math Comput Appl 21:20Google Scholar
  22. Kisi O (2008) Streamflow forecasting using neuro-wavelet technique. Hydrol Process 22:4142–4152CrossRefGoogle Scholar
  23. Kisi O (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng 14(8):773–782CrossRefGoogle Scholar
  24. Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24:211–231CrossRefGoogle Scholar
  25. Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140CrossRefGoogle Scholar
  26. Kisi O, Partal T (2011) Wavelet and neuro-fuzzy conjunction model for streamflow forecasting. Hydrol Res 42:447–456CrossRefGoogle Scholar
  27. Labat D (2005) Recent advances in wavelet analyses: part 1. A review of concepts. J Hydrol 314:275–288CrossRefGoogle Scholar
  28. Labat D, Ababou R, Mangin A (2000) Rainfall–runoff relations for karstic springs. part II: continuous. J Hydrol 248:149–278Google Scholar
  29. Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241CrossRefGoogle Scholar
  30. Maheswaran R, Khosa R (2012) Comparative study of different wavelets for hydrologic forecasting. Comput Geosci 46:284–295CrossRefGoogle Scholar
  31. Makwanana JJ, Tiwari MK (2014) Intermittent streamflow forecasting and extreme event modelling using wavelet based artificial neural networks. Water Resour Res 28:4857–4873Google Scholar
  32. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11: 674–693.  https://doi.org/10.1109/34.192463 CrossRefGoogle Scholar
  33. Nalley D, Adamowski J, Khalil B (2012) Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008). J Hydrol 475:204–228CrossRefGoogle Scholar
  34. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  35. Nason G, Sachs R, Krois G (2000) Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. J R Stat Soc B 62:271–292CrossRefGoogle Scholar
  36. Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour Manag 23:2877–2894CrossRefGoogle Scholar
  37. Nourani V, Kisi O, Komasi M (2011) Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J Hydrol 402:41–59CrossRefGoogle Scholar
  38. Nourani V, BagahnamA Adamowski J, Kisi O (2014) Applications of hybrid wavelet-artificial intelligence models in hydrology: a review. J Hydrol 514:358–377CrossRefGoogle Scholar
  39. Pagano TC, Garen DC, Perkins TR, Pasteris PA (2009) Daily updating of operational statistical seasonal water supply forecasts for the western US. J Am Water Resour Assoc 45:767–778CrossRefGoogle Scholar
  40. Partal T, Küçük M (2006) Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey). Phys Chem Earth 31:1189–1200CrossRefGoogle Scholar
  41. Percival DB (2008) Analysis of geophysical time series using discrete wavelet transforms: an overview. In: Donner RV, Barbosa SM (eds) Nonlinear time series analysis in the geosciences–applications in climatology, geodynamics, and solar-terrestrial physics, vol 112, pp 61–79Google Scholar
  42. Robertson DE, Wang QJ (2012) A Bayesian approach to predictor selection for seasonal streamflow forecasting. J Hydrometeorol.  https://doi.org/10.1175/JHM-D-10-05009.1 Google Scholar
  43. Rogers WF (1982) Some characteristics and implications of drainage basin linearity and nonlinearity. J Hydrol 55:247–265CrossRefGoogle Scholar
  44. Sang YF (2012) A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resour Manag 26:3345–3365CrossRefGoogle Scholar
  45. Santos CAG, Silva GBL (2014) Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrol Sci J 59:1–13CrossRefGoogle Scholar
  46. Santos CAG, Freire PKMM, Silva GBL, Silva RM (2014) Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir. Proc Int Assoc Hydrol Sci 364:100–105Google Scholar
  47. Seo Y, Kim S, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243.  https://doi.org/10.1016/j.jhydrol.2014.11.050 CrossRefGoogle Scholar
  48. Shoaib M, Shamseldin AY, Melville BW (2014) Comparative study of different wavelet based neural network models for rainfall–runoff modelling. J Hydrol 515:47–58CrossRefGoogle Scholar
  49. Shoaib M, Shamseldin AY, Khan S, Khan MM, Khan ZM, Sultan T, Melville BW (2017) A comparative study of various hybrid wavelet feed forward neural network models for runoff forecasting. Water Resour Res.  https://doi.org/10.1007/s11269-017-1796-1 Google Scholar
  50. Sun AY, Wang D, Xu X (2014) Monthly streamflow forecasting using Gaussian process regression. J Hydrol 511:72–81CrossRefGoogle Scholar
  51. Toth E, Brath A (2007) Multistep ahead streamflow forecasting: role of calibration data in conceptual and neural network modelling. Water Resour Res.  https://doi.org/10.1029/2006WR005383 Google Scholar
  52. Van Ogtrop FF, Vervoort RW, Heller GZ, Stasinopoulos DM, Rigby RA (2011) Long-range forecasting of intermittent streamflow. Hydrol Earth Syst Sci 15:3343–3354CrossRefGoogle Scholar
  53. Wang D, Wu L (2012) Similarity between runoff coefficient and perennial stream density in the Budyko framework. Hydrol Earth Syst Sci 09:7571–7589CrossRefGoogle Scholar
  54. Wang W, Van Gelder Pieter HAJM, Vrijlingb JK, Mac Jun (2006a) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324:383–399CrossRefGoogle Scholar
  55. Wang W, Vrijling JK, Van Gelder PHAJM, Ma J (2006b) Testing for nonlinearity of streamflow processes at different timescales. J Hydrol 322:247–268CrossRefGoogle Scholar
  56. Wei S, Yang H, Song J, Abbaspour K, Xu Z (2014) A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrol Sci J.  https://doi.org/10.1080/02626667.2012.754102 Google Scholar
  57. Williams JR, Amaratunga K (1994) Introduction to wavelets in engineering. Int J Numer Methods Eng 37:2365–2388CrossRefGoogle Scholar
  58. Xiaoyu L, Bing WK, Simon YF (1999) Time series prediction based on fuzzy principles. Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310Google Scholar
  59. Yonaba H, Anctil F, Fortin V (2010) Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J Hydrol Eng 15:275–283CrossRefGoogle Scholar
  60. Zakhrouf M, Bouchelkia H, Stamboul M, Kim S, Heddam S (2018) Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou river (Algeria). Phys Geogr.  https://doi.org/10.1080/02723646.2018.1429245 Google Scholar
  61. Zhang GP, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.LEGHYD Laboratory, Department of Civil EngineeringUniversity of Science and Technology Houari BoumedieneBab-Ezzouar, AlgiersAlgeria

Personalised recommendations