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

• Published in: Water resources management 2013, Vol.27 (1), p.1-23
• Main Authors: Ishak, Asnor Muizan, Remesan, Renji, Srivastava, Prashant K., Islam, Tanvir, Han, Dawei
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Atmospheric Sciences; Civil Engineering; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environment; Error correction & detection; Exact sciences and technology; Feature selection; Geotechnical Engineering & Applied Earth Sciences; Humidity; Hydrogeology; Hydrologic data; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; Learning theory; Mathematical analysis; Mathematical models; Meteorology; Modelling; Neural networks; Power; Radiation; Rain; Regression; Regression analysis; Relative humidity; Solar radiation; Statistical analysis; Studies; Support vector machines; Variables; Velocity; Water resources management; Weather forecasting; Wind speed
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-012-0130-1

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.