Advertisement

Water Resources Management

, Volume 32, Issue 2, pp 527–545 | Cite as

New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

  • Saeid Mehdizadeh
  • Javad Behmanesh
  • Keivan Khalili
Article

Abstract

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R2). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.

Keywords

Estimation Rainfall GEP-ARCH ANN-ARCH 

Notes

Acknowledgements

The authors of the paper would like to thank the anonymous reviewers for their constructive comments, as well as the Islamic Republic of Iran Meteorological Organization (IRIMO) to provide the monthly rainfall data for the present study.

References

  1. Abbot J, Marohasy J (2014) Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos Res 138:166–178CrossRefGoogle Scholar
  2. Ashraf B, Yazdani R, Mousavi-Baigy M, Bannayan M (2014) Investigation of temporal and spatial climate variability and aridity of Iran. Theor Appl Climatol 118(1–2):35–46CrossRefGoogle Scholar
  3. Behmanesh J, Mehdizadeh S (2017) Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region. Environ Earth Sci.  https://doi.org/10.1007/s12665-017-6395-1
  4. Chinchorkar SS, Patel GR, Sayyad FG (2012) Development of monsoon model for long range forecast rainfall explored for Anand (Gujarat-India). Int J Water Resour Environ Eng 4(11):322–326Google Scholar
  5. Delleur JW, Karamouz M (1982) Uncertainty in reservoir operation. Optimal Allocation of Water Resources (Proceedings of the Fxeter Symposium), IAHS Publication no. 135:7–16Google Scholar
  6. Ebtehaj I, Bonakdari H, Zaji AH, Azimi H, Sharifi A (2015) Gene expression programming to predict the discharge coefficient in rectangular side weirs. Appl Soft Comput 35:618–628CrossRefGoogle Scholar
  7. Engle RF (1982) Autoregressive conditional heteoscedasticity with estimates of the variance of United Kingdom inflations. Econometrica 50(4):987–1007CrossRefGoogle Scholar
  8. Feng Q, Wen X, Li J (2015) Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resour Manag 29(4):1049–1065CrossRefGoogle Scholar
  9. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129Google Scholar
  10. Gocic M, Motamedi S, Shamshirband S, Petkovic D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput Electron Agric 113:164–173CrossRefGoogle Scholar
  11. Haykin S (1998) Neural networks-a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River, pp 26–32Google Scholar
  12. He X, Guan H, Qin J (2015) A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. J Hydrol 527:88–100CrossRefGoogle Scholar
  13. Imani M, You RJ, Kuo CY (2014) Forecasting Caspian Sea level changes using satellite altimetry data (June 1992–December 2013) based on evolutionary support vector regression algorithms and gene expression programming. Glob Planet Chang 121:53–63CrossRefGoogle Scholar
  14. Kashid SS, Maity R (2012) Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming. J Hydrol 454–455:26–41Google Scholar
  15. Khalili K, Nazeri Tahroudi M, Mirabbasi R, Ahmadi F (2016) Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stoch Environ Res Risk Assess 30(4):1205–1221Google Scholar
  16. Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25(4):783–792CrossRefGoogle Scholar
  17. Marti P, Shiri J, Duran-Ros M, Arbat G, de Cartagena FR, Puig-Bargues J (2013) Artificial neural networks vs. Gene Expression Programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Comput Electron Agric 99:176–185CrossRefGoogle Scholar
  18. Mehdizadeh S, Behmanesh J, Khalili K (2016) Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation. J Atmos Sol Terr Phys 146:215–227CrossRefGoogle Scholar
  19. Mehdizadeh S, Behmanesh J, Khalili K (2017) Application of gene expression programming to predict daily dew point temperature. Appl Therm Eng 112:1097–1107CrossRefGoogle Scholar
  20. Mekanik F, Imteaz MA, Gato-Trinidad S, Elmahdi A (2013) Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21CrossRefGoogle Scholar
  21. Moustris KP, Larissi IK, Nastos PT, Paliatsos AG (2011) Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resour Manag 25(8):1979–1993CrossRefGoogle Scholar
  22. Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342(1–2):199–212CrossRefGoogle Scholar
  23. Ramirez MCV, de Campos Velho HF, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. J Hydrol 301(1–4):146–162CrossRefGoogle Scholar
  24. Shenify M, Danesh AS, Gocic M, Taher RS, Abdul Wahab AW, Ghani A, Shamshirband S, Petkovic D (2016) Precipitation estimation using support vector machine with discrete wavelet transform. Water Resour Manag 30(2):641–652CrossRefGoogle Scholar
  25. Shiri J, Keshavarzi A, Kisi O, Iturraran-Viveros U, Bagherzadeh A, Mousavi R, Karimi S (2017) Modeling soil cation exchange capacity using soil parameters: assessing the heuristic models. Comput Electron Agric 135:242–251CrossRefGoogle Scholar
  26. UNEP (1992) World atlas of desertification. The united nations environment programme (UNEP), LondonGoogle Scholar
  27. Venkata Ramana R, Krishna B, Kumar SR, Pandey NG (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27(10):3697–3711CrossRefGoogle Scholar
  28. Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389(1–2):146–167CrossRefGoogle Scholar
  29. Yassin MA, Alazba AA, Mattar MA (2016) A new predictive model for furrow irrigation infiltration using gene expression programming. Comput Electron Agric 122:168–175CrossRefGoogle Scholar
  30. Zanetti SS, Sousa EF, Oliveira VP, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng 133(2):83–89CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Saeid Mehdizadeh
    • 1
  • Javad Behmanesh
    • 1
  • Keivan Khalili
    • 1
  1. 1.Department of Water EngineeringUrmia UniversityUrmiaIran

Personalised recommendations