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Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach

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  • Asnor Ishak
  • Renji Remesan
  • Prashant Srivastava
  • Tanvir Islam
  • Dawei Han

Abstract

Accurate estimation of wind speed is essential for many hydrological applications. One way to generate wind velocity is from the fifth generation PENN/NCAR MM5 mesoscale model. However, there is a problem in using wind speed data in hydrological processes due to large errors obtained from the mesoscale model MM5. The theme of this article has been focused on hybridization of MM5 with four mathematical models (two regression models- the multiple linear regression (MLR) and the nonlinear regression (NLR), and two artificial intelligence models – the artificial neural network (ANN) and the support vector machines (SVMs)) in such a way so that the properly modelled schemes reduce the wind speed errors with the information from other MM5 derived hydro-meteorological parameters. The forward selection method was employed as an input variable selection procedure to examine the model generalization errors. The input variables of this statistical analysis include wind speed, temperature, relative humidity, pressure, solar radiation and rainfall from the MM5. The proposed conjunction structure was calibrated and validated at the Brue catchment, Southwest of England. The study results show that relatively simple models like MLR are useful tools for positively altering the wind speed time series obtaining from the MM5 model. The SVM based hybrid scheme could make a better robust modelling framework capable of capturing the non-linear nature than that of the ANN based scheme. Although the proposed hybrid schemes are applied on error correction modelling in this study, there are further scopes for application in a wide range of areas in conjunction with any higher end models. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Asnor Ishak & Renji Remesan & Prashant Srivastava & Tanvir Islam & Dawei Han, 2013. "Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 1-23, January.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:1:p:1-23
    DOI: 10.1007/s11269-012-0130-1
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    1. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    2. Seema Chauhan & R. Shrivastava, 2009. "Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 825-837, March.
    3. Kashyap, P. S. & Panda, R. K., 2001. "Evaluation of evapotranspiration estimation methods and development of crop-coefficients for potato crop in a sub-humid region," Agricultural Water Management, Elsevier, vol. 50(1), pages 9-25, August.
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    Cited by:

    1. Prashant Srivastava & Tanvir Islam & Manika Gupta & George Petropoulos & Qiang Dai, 2015. "WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-Meteorological Variables," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2267-2284, May.
    2. Tanvir Islam & Prashant Srivastava & Miguel Rico-Ramirez & Qiang Dai & Manika Gupta & Sudhir Singh, 2015. "Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1473-1495, April.
    3. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.
    4. Tanvir Islam & Prashant K. Srivastava & Dinesh Kumar & George P. Petropoulos & Qiang Dai & Lu Zhuo, 2016. "Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 845-855, June.
    5. Prashant Srivastava & Dawei Han & Miguel Rico-Ramirez & Deleen Al-Shrafany & Tanvir Islam, 2013. "Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5069-5087, December.

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