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Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms

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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.

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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.

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Correspondence to Sutapa Chaudhuri or Anirban Middey.

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Responsible editor: M. Kaplan.

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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

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