Advertisement

Computation of Daily Solar Radiation Using Wavelet and Support Vector Machines: A Case Study

  • Sungwon Kim
  • Youngmin Seo
  • Vijay P. Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 382)

Abstract

The objective of this study is to apply a hybrid model for estimating solar radiation and investigate its accuracy. A hybrid model is wavelet-based support vector machines (WSVMs). Wavelet decomposition is employed to decompose the solar radiation time series components into approximation and detail components. These decomposed time series are then used as input of support vector machines (SVMs) modules in the WSVMs model. Based on statistical indexes, results indicate that WSVMs can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois.

Keywords

Support vector machines Wavelet decomposition Solar radiation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allen, R.G., Pereira, L.S., Raes, D., Smith, M.: Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage. Paper No. 56. Food and Agriculture Organization of the United Nations, Rome (1998)Google Scholar
  2. 2.
    ASCE Task Committee: Criteria for evaluation of watershed models. J. Irrig. Drain. Eng. 119(3), 429–442 (1993)CrossRefGoogle Scholar
  3. 3.
    Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans. Power Syst. 26(1), 137–144 (2011)CrossRefGoogle Scholar
  4. 4.
    Dawson, C.W., Wilby, R.L.: Hydrological modelling using artificial neural networks. Prog. phys. Geog. 25(1), 80–108 (2001)CrossRefGoogle Scholar
  5. 5.
    González-Audícana, M., Otazu, X., Fors, O., Seco, A.: Comparison between Mallat’s and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. Int. J. Remote Sens. 26(3), 595–614 (2005)CrossRefGoogle Scholar
  6. 6.
    Haykin, S.: Neural networks and learning machines, 3rd edn. Prentice Hall, NJ (2009)Google Scholar
  7. 7.
    Izadifar, Z., Elshorbagy, A.: Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models. Hydrol. Process. 24(23), 3413–3425 (2010)CrossRefGoogle Scholar
  8. 8.
    Kim, S., Shiri, J., Kisi, O.: Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour. Manag. 26(11), 3231–3249 (2012)CrossRefGoogle Scholar
  9. 9.
    Kim, S., Seo, Y., Singh, V.P.: Assessment of pan evaporation modeling using bootstrap resampling and soft computing methods. J. Comput. Civ. Eng. (2013a). doi: 10.1061/(ASCE)CP.1943-5487.0000367
  10. 10.
    Kim, S., Shiri, J., Kisi, O., Singh, V.P.: Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resour. Manag. 27(7), 2267–2286 (2013b)Google Scholar
  11. 11.
    Legates, D.R., McCabe, G.J.: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35(1), 233–241 (1999)CrossRefGoogle Scholar
  12. 12.
    Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern. Anal. Mach. Intell. 11(7), 674–693 (1989)zbMATHCrossRefGoogle Scholar
  13. 13.
    Nason, G.: Wavelet methods in statistics with R. Springer, NY (2010)Google Scholar
  14. 14.
    Nash, J.E., Sutcliffe, J.V.: River flow forecasting through conceptual models, Part 1 – A discussion of principles. J. Hydrol. 10(3), 282–290 (1970)CrossRefGoogle Scholar
  15. 15.
    Nejad, F.H., Nourani, V.: Elevation of wavelet denoising performance via an ANN-based streamflow forecasting model. Int. J. Comput. Sci. Manag. Res. 1(4), 764–770 (2012)Google Scholar
  16. 16.
    Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and adaptive systems: fundamentals through simulation. Wiley, John & Sons Inc., NY (2000)Google Scholar
  17. 17.
    Tripathi, S., Srinivas, V.V., Nanjundish, R.S.: Downscaling of precipitation for climate change scenarios: a support vector machine approach. J. Hydrol. 330(3–4), 621–640 (2006)CrossRefGoogle Scholar
  18. 18.
    Vapnik, V.N.: The nature of statistical learning theory, 2nd edn. Springer, NY (2010)Google Scholar
  19. 19.
    van Bavel, C.H.M.: Estimating soil moisture conditions and time for irrigation with the evapotranspiration method. USDA, ARS 41–11, U.S. Dept. of Agric., Raleigh, NC, 1–16 (1956)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Railroad and Civil EngineeringDongyang UniversityYeongjuRepublic of Korea
  2. 2.Department of Constructional Disaster Prevention EngineeringKyungpook National UniversitySangjuRepublic of Korea
  3. 3.Department of Biological and Agricultural Engineering & Zachry Department of Civil EngineeringTexas A & M UniversityCollege StationUSA

Personalised recommendations