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

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Harmony Search Algorithm

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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References

  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. ASCE Task Committee: Criteria for evaluation of watershed models. J. Irrig. Drain. Eng. 119(3), 429–442 (1993)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Dawson, C.W., Wilby, R.L.: Hydrological modelling using artificial neural networks. Prog. phys. Geog. 25(1), 80–108 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Haykin, S.: Neural networks and learning machines, 3rd edn. Prentice Hall, NJ (2009)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  12. Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern. Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  13. Nason, G.: Wavelet methods in statistics with R. Springer, NY (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  18. Vapnik, V.N.: The nature of statistical learning theory, 2nd edn. Springer, NY (2010)

    Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Department of Railroad and Civil Engineering, Dongyang University, Yeongju, 750-711, Republic of Korea

    Sungwon Kim

  2. Department of Constructional Disaster Prevention Engineering, Kyungpook National University, Sangju, 742-711, Republic of Korea

    Youngmin Seo

  3. Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, TX, 77843-2117, USA

    Vijay P. Singh

Authors
  1. Sungwon Kim
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  2. Youngmin Seo
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  3. Vijay P. Singh
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Corresponding author

Correspondence to Sungwon Kim .

Editor information

Editors and Affiliations

  1. Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  2. Energy IT, Gachon University, Seongnam-Si, Korea (Republic of)

    Zong Woo Geem

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© 2016 Springer-Verlag Berlin Heidelberg

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Kim, S., Seo, Y., Singh, V.P. (2016). Computation of Daily Solar Radiation Using Wavelet and Support Vector Machines: A Case Study. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_27

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  • DOI: https://doi.org/10.1007/978-3-662-47926-1_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47925-4

  • Online ISBN: 978-3-662-47926-1

  • eBook Packages: EngineeringEngineering (R0)

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