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Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data

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Abstract

In this study, three different neural network algorithms (feed forward back propagation, FFBP; radial basis function; generalized regression neural network) and wavelet transformation were used for daily precipitation predictions. Different input combinations were tested for the precipitation estimation. As a result, the most appropriate neural network model was determined for each station. Also linear regression model performance is compared with the wavelet neural networks models. It was seen that the wavelet FFBP method provided the best performance evaluation criteria. The results indicate that coupling wavelet transforms with neural network can provide significant advantages for estimation process. In addition, global wavelet spectrum provides considerable information about the structure of the physical process to be modeled.

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References

  • Adamovski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40

    Article  Google Scholar 

  • Altun A, Bilgil A, Fidan BC (2007) Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Syst Appl 32:599–605

    Article  Google Scholar 

  • Anctil F, Tape DG (2004) An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J Environ Eng Sci 3:121–128

    Article  Google Scholar 

  • Applequist S, Gahrs GE, Pfeffer RL (2002) Comparison of methodologies for probabilistic quantitative precipitation forecasting. Am Meteorol Soc 17:783–799

    Google Scholar 

  • Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J Hydrol 508:418–429

    Article  Google Scholar 

  • Bromhead D, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    Google Scholar 

  • Bustamante J, Gomes J, Chou S, Rozante J (1999) Evaluation of April 1999 rainfall forecasts over South America using the ETA model. INPE, Cachoeria Paulista, Sp, Brasil

    Google Scholar 

  • Cigizoglu HK (2003) Estimation, forecasting and extrapolation of flow data by artificial neural networks. Hydrol Sci J 48(3):349–361

    Article  Google Scholar 

  • Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi layer perceptrons. Adv Water Res 27:185–195

    Article  Google Scholar 

  • Cigizoglu HK (2005) Generalized regression neural network in monthly flow forecasting. Civ Eng Environ Syst 22(2):71–84

    Article  Google Scholar 

  • Cigizoglu HK, Alp M (2004) Rainfall–runoff modelling using three neural network methods, artificial intelligence and soft computing—ICAISC 2004. Lecture notes in artificial intelligence, vol 3070, pp 166–171

  • Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68

    Article  Google Scholar 

  • Coulibaly P, Burn HD (2004) Wavelet analysis of variability in annual Canadian streamflows. Water Resour Res 40:W03105

    Google Scholar 

  • Dabechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36(5):961–1005

    Article  Google Scholar 

  • Freiwan M, Cigizoglu HK (2005) Prediction of total monthly rainfall in Jordan using feed forward backpropagation method. Fresenious Environ Bull 14(2):142–151

    CAS  Google Scholar 

  • Gao C, Gemmer M, Zeng X, Liu B, Su B, Wen Y (2009) Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network. Stoch Environ Res Risk Assess 24(5):685–697

    Article  Google Scholar 

  • Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7:585–592

    Article  Google Scholar 

  • Jayawardena AW, Fernando DAK (1998) Use of radial basis function type artificial neural networks for runoff simulation. Comput Aided Civ Infrastruct Eng 13:91–99

    Article  Google Scholar 

  • Kermania MZ, Kisi O, Rajaeec T (2013) Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Appl Soft Comput 13(12):4633–4644

    Article  Google Scholar 

  • Kim TW, Valdes JB (2003) Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng ASCE 6:319–328

    Article  Google Scholar 

  • Kisi O (2006) Generalized regression neural networks for evapotranspiration modeling. Hydrol Sci J 51(6):1092–1105

    Article  Google Scholar 

  • Kisi O (2011) A combined generalized regression neural network wavelet model for monthly streamflow prediction. KSCE J Civ Eng 15(8):1469–1479

    Article  Google Scholar 

  • Kulligowski RJ, Barros AP (1998) Localized precipitation from a numerical weather prediction model using Artificial Neural Networks. Weather Forecast 13:1195–1205

    Article  Google Scholar 

  • Kumar ARS, Sudheer KP, Jain SK (2005) Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrol Process 19(6):1277–1291

    Article  Google Scholar 

  • Mallat SG (1989) A theory for multi resolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  • Meyer Y (1993) Wavelets algorithms & applications. Society for Industrial and Applied Mathematics, Philadelphia

    Google Scholar 

  • Mishra AK, Özger M, Singh VP (2011) Wet and dry spell analysis of global climate model-generated precipitation using power laws and wavelet transforms. Stoch Environ Res Risk Assess 25(4):517–535

    Article  Google Scholar 

  • Olson DA, Junker NW, Korty B (1995) Evaluation 33 years of quantitative precipitation forecasting at NMC. Weather Forecast 10:498–511

    Article  Google Scholar 

  • Olson J, Uvo CB, Jinno K, Kawamura A, Nishiyama K, Koreeda N, Nakashima T, Morita O (2004) Neural networks for rainfall forecasting by atmospheric downscaling. J Hydrol Eng ASCE 9:1–12

    Article  Google Scholar 

  • Partal T (2009) River flow forecasting using different artificial neural networks algorithms and wavelet transform. Can J Civ Eng 36:26–39

    Article  Google Scholar 

  • Partal T, Cigizoglu HK (2009) Prediction of daily precipitation using wavelet-neural networks. Hydrol Sci J 54:234–246

    Article  Google Scholar 

  • Ramana RV, Krishna B, Kumar SR, Pandey NG (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711

    Article  Google Scholar 

  • Ramirez MCV, Velho HFC, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. J Hydrol 301:146–162

    Article  Google Scholar 

  • Shoaib M, Shamseldin AY, Melville BW (2014) Comparative study of different wavelet based neural network models for rainfall–runoff modeling. J Hydrol 515:47–58

    Article  Google Scholar 

  • Silverman D, Dracup JA (2000) Artificial neural networks and long-range precipitation prediction in California. J Appl Meteorol 39(1):57–66

    Article  Google Scholar 

  • Smith LC, Turcotte DL, Isacks B (1998) Stream flow characterization and feature detection using a discrete wavelet transform. Hydrol Process 12:233–249

    Article  Google Scholar 

  • Sreekanth P, Geethanjali DN, Sreedevi PD, Ahmed S, Kumar NR, Jayanthi PDK (2009) Forecasting groundwater level using artificial neural networks. Curr Sci 96(7):933–939

    Google Scholar 

  • Sudheer KP, Jain A (2003) Radial basis function neural network for modeling rating curves. J Hydrol Eng ASCE 8(3):161–164

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Rangan DM, Saheb SM (2002) Modeling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202

    Article  Google Scholar 

  • Tatlı H, Dalfes HN, Menteş ŞS (2004) A statistical downscaling method for monthly total precipitation over Turkey. Int J Climatol 24:161–180

    Article  Google Scholar 

  • Torrence C, Compo GP (1997) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79:61–78

    Article  Google Scholar 

  • Unal NE, Aksoy A, Akar T (2004) Annual and monthly rainfall data generation schemes. Stoch Env Research and Risk Assessment 18:245–257

    Google Scholar 

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

    Google Scholar 

Download references

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Authors and Affiliations

  1. Civil Engineering Department, Engineering Faculty, Ondokuz Mayis University, Samsun, Turkey

    Turgay Partal

  2. Hydraulics Division, Civil Engineering Department, Istanbul Technical University, 34469, Maslak, Istanbul, Turkey

    H. Kerem Cigizoglu & Ercan Kahya

Authors
  1. Turgay Partal
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  2. H. Kerem Cigizoglu
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  3. Ercan Kahya
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Correspondence to Turgay Partal.

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Partal, T., Cigizoglu, H.K. & Kahya, E. Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stoch Environ Res Risk Assess 29, 1317–1329 (2015). https://doi.org/10.1007/s00477-015-1061-1

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  • DOI: https://doi.org/10.1007/s00477-015-1061-1

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