Abstract
The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The pre-monsoon thunderstorms during 1997–2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12 h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.
Similar content being viewed by others
References
Abraham A, Philip NS, Joseph B (2001) Will we have a wet summer? Long term rain forecasting using Soft Computing models. In: Kerchoffs EJH, Snorek M (eds) Modeling and simulation 2001, Publication of the Society for Computer Simulation International, Prague, pp 1044–1048
Bishop CM (1995) neural networks for pattern recognition. Oxford University Press, Oxford. ISBN 0-19-853849-9
Coulibaly P, Bobée B, Anctil F (2001) Improving extreme hydrologic events forecasting using a new criterion for ANN selection. Hydrol Process 15(8):1533–1536
Chaudhuri S (2005) Genetic algorithm to recognize apt energy for the genesis of severe thunderstorms, Vatabaran. AFAC J Meteorol 29(2):1–8
Chaudhuri S (2006) A hybrid model to estimate the depth of potential convective instability during severe thunderstorms. Soft Comput 10:243–248
Chaudhuri S (2008a) Identification of the level of downdraft formation during severe thunderstorms: a frequency domain analysis. Meteorol Atmos Phys 102:123–129
Chaudhuri S (2008b) Preferred type of cloud in the genesis of severe thunderstorms—a soft computing approach. Atmos Res 88:149–156
Chaudhuri S, Middey A (2009) Applicability of bipartite graph model for thunderstorms forecast over Kolkata. Adv Meteorol 2009:1–12
Chaudhuri S (2010a) Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm. Asia Pac J Atmos Sci (Springer) 46(2):173–183
Chaudhuri S (2010b) Predictability of severe thunderstorms with fractal dimension approach. Asian J Water Air Environ Pollut (IOP) 7(4):81–87
Chaudhuri S, Middey A (2011) Nowcasting thunderstorms with graph spectral distance and entropy estimation. Met Appl (RMS) 18:238–249
Desai BN, Rao YP (1954) On the cold pools and their role in the development of Nor’westers over West Bengal and East Pakistan. Ind J Met Geophys 5:243–248
El-Shafie A, Reda Taha M, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manage 21:533–556
Elshorbagy A, Simonovic SP (2000) Performance evaluation of artificial neural networks for runoff prediction. J Hydrol Eng ASCE 5(4):424–427
Fernando DA, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng ASCE 3(3):203–209
Gardner MW, Dorling SR (1998) Artificial neural network (multilayer perceptron)—a review of application in atmospheric sciences. Atmos Environ 32:2627–2636
Ghosh S, Sen PK, De UK (1999) Identification of significant parameters for the prediction of pre-monsoon thunderstorms at Calcutta. Int J Climatol 19:673–681
Hsieh WW, Tang T (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull Am Meteorol Soc 79:1855–1869
Jang Roger J-S (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Jin L, Yao C, Huang YX (2008) A nonlinear artificial intelligence ensemble prediction model for typhoon intensity. Mon Weather Rev 136:4541–4554
Jolliffe IT, Stephenson DB (2003) Forecast Verification: a practitioner’s guide in atmospheric science. Wiley, West Sussex
Kandalgaokar SS, Tinmaker MIR, Kulkarni MK, Nath A (2002) Thunderstorm activity and sea surface temperature over the Island stations and along the east and west coast of India. Mausam 53:245
Kuligowski RJ, Barros AP (1998) Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather Forecast 13:1194–1204
Lawrence, J (1994) Introduction to neural networks. California Scientific Software Press, California. ISBN 1-883157-00-5.
Linhart H (1960) A criterion for selecting variables in a regression analysis. Psychometrika 25(1):45–58
Manohar GK, Kandalgaokar SS, Tinmaker MIR (1999) Thunderstorm activity over India and Indian south-west monsoon. J Geophys Res 104:4169
Maqsood I, Khan MR, Huang GH, Abdalla R (2005) Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada. J Eng Appl Artif Intell 18(1):115–125
Marzban C, Stumpf G (1998) A neural network for damaging wind prediction. Weather Forecast 13:151–163
Marzban C, Witt A (2001) A Bayesian neural network for severe hail size prediction. Weather Forecast 16:600–610
McCann DW (1992) A neural network short-term forecast of significant thunderstorms. Weather Forecast 7:525–534
Perez P, Reyes J (2001) Prediction of particulate air pollution using neural techniques. Neural Comput Appl 10:165–171
Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall–runoff relationship with artificial neural network. J Hydrol 285(1–4):96–113
Seker H, Odetayo MO, Petrovic D, Naguib RNG (2003) A fuzzy logic based method for prognostic decision making in breast and prostate cancers. IEEE Trans Inf Technol Biomed 7(2):114–122
Shao J (1997) Improving nowcasts of road surface temperature by a backpropagation neural network. Weather Forecast 13:164–171
Tokar AS, Markus M (2000) Precipitation–runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156–161
Wang Y, Yu TY, Yeary M, Shapiro A, Nemati S, Foster M, Andra DL Jr, Jain M (2008) Tornado detection using a neuro–fuzzy system to integrate shear and spectral signatures. J Atmos Ocean Technol 25(7):1136–1148
Wasserman PD, Schwartz T (1988) Neural networks. II. What are they and why is everybody so interested in them now? IEEE Expert 3(1):10–15
Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, Oxford
Zealand CM, Burn DH, Simonovic SP (1999) Short term stream flow forecasting using artificial neural networks. J Hydrol (Amsterdam) 214:32–48
Acknowledgments
The first author acknowledges the financial assistance rendered by the Department of Science & Technology, Govt. of India for conducting the research and Indian Meteorological Department (IMD) for providing the data and thunderstorm records. The authors would like to express special gratitude to the anonymous reviewers for their substantial remarks that increased the completeness and clarity of the manuscript. The guidance of Dr. M. Kaplan during the revision of the paper is greatly appreciated.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Responsible editor: M. Kaplan.
Rights and permissions
About this article
Cite this article
Chaudhuri, S., Middey, A. Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms. Meteorol Atmos Phys 114, 139 (2011). https://doi.org/10.1007/s00703-011-0158-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00703-011-0158-4