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.
Similar content being viewed by others
References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome
Bray M, Han D (2004) Identification of support vector machines for runoff modelling. J Hydroinf 6(4):265–280
Broyden CG (1970) The convergence of a class of double-rank minimization algorithms 1. General considerations. IMA J Appl Math 6(1):76–90
Cawley GC, Talbot NLC (2003) Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recogn 36(11):2585–2592
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
Chauhan S, Shrivastava R (2009) Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks. Water resour manag 23(5):825–837
Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129(4):569–585
Chen ST, Yu PS (2007) Real-time probabilistic forecasting of flood stages. J Hydrol 340(1–2):63–77
Cristianini N, Shawe-Taylor J (2000) An introduction to support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Dudhia J (1993) A nonhydrostatic version of the Penn State-NCAR mesoscale model: validation tests and simulation of an atlantic cyclone and cold front. Mon Weather Rev 121(5):1493–1513
Efron B (1986) How biased is the apparent error rate of a prediction rule? J Am Stat Assoc 81(394):461–470
Fletcher R (1987) Practical Methods of Optimization, second edition. John Wiley, Chichester
Frank WM (1983) The cumulus parameterization problem. Mon Weather Rev 111:1859–1871
Grell GA (1995) A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), NCAR Technical Note, Colorado and Pennsylvania, USA
Grell GA, Dudhia J, Stauffer DR, Mesoscale NCfAR, Dicision MM (1994) A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), NCAR Technical Note, Colorado and Pennsylvania, USA
Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics. Springer-Verlag, New York, p 533
Haugh LD, Box GEP (1977) Identification of dynamic regression (distributed lag) models connecting two time series. J Am Stat Assoc 72(357):121–130
Ishak AM, Bray M, Remesan R, Han D (2010) Estimating reference evapotranspiration using numerical weather modelling. Hydrol Processes 24(24):3490–3509
Ishak AM, Bray M, Remesan R, Han D (2012) Seasonal evaluation of rainfall estimation by four cumulus parameterization schemes and their sensitivity analysis. Hydrol Processes 26(7):1062–1078
Islam T, Rico-Ramirez MA, Han D, Srivastava PK (2012a) Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos Res 109–110:95–113
Islam T, Rico-Ramirez MA, Thurai M, Han D (2012b) Characteristics of raindrop spectra as normalized gamma distribution from a Joss–Waldvogel disdrometer. Atmos Res 108:57–73
Kashyap PS, Panda R (2001) Evaluation of evapotranspiration estimation methods and development of crop-coefficients for potato crop in a sub-humid region. Agric Water Manag 50(1):9–25
Mass CF, Kuo YH (1998) Regional real-time numerical weather prediction: current status and future potential. Bull Am Meteorol Soc 79(2):253–264
Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural netw 6(4):525–533
Rao V, Rao H (1996) C++ Neural networks and fuzzy logic, BPB. New Delhi, India, pp 380–381.
Salcedo-Sanz S, Pérez-Bellido ÁM, Ortiz-García EG, Portilla-Figueras A, Prieto L, Paredes D (2009) Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction. Renew Energy 34(6):1451–1457
Thomas DM, Benson MA (1970) Generalization of streamflow characteristics from drainage-basin characteristics. U. S. Geol. Surv. Water Supply Pap. 1975:55
Vapnik V (1998) The support vector method of function estimation. Nonlinear Model Adv Black-Box Tech 55:86
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3):294–306
Warner TT, Kibler DF, Steinhart RL (1991) Separate and coupled testing of meteorological and hydrological forecast models for the Susquehanna river basin in Pennsylvania. J Appl Meteorol 30:1521–1533
Yang K, Huang G, Tamai N (2001) A hybrid model for estimating global solar radiation. Sol Energy 70(1):13–22
Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3):704–716
Zhong S, Fast J (2003) An evaluation of the MM5, RAMS, and Meso-Eta models at subkilometer resolution using VTMX field campaign data in the Salt Lake Valley. Mon Weather Rev 131(7):1301–1322
Zhu YM, Lu X, Zhou Y (2007) Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang river in the Upper Yangtze Catchment, China. Geomorphology 84(1):111–125
Acknowledgements
This research is funded by the Public Services Department of Malaysian Government. We also acknowledge the support from the Irrigation and Drainage Department, Malaysian Government. Many thanks are expressed to the anonymous reviewers for their valuable comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ishak, A.M., Remesan, R., Srivastava, P.K. et al. Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach. Water Resour Manage 27, 1–23 (2013). https://doi.org/10.1007/s11269-012-0130-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-012-0130-1