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Natural Hazards

, Volume 93, Issue 1, pp 237–247 | Cite as

Shannon entropy maximization supplemented by neurocomputing to study the consequences of a severe weather phenomenon on some surface parameters

  • Surajit Chattopadhyay
  • Goutami Chattopadhyay
  • Subrata Kumar Midya
Original Paper

Abstract

An information theoretic approach based on Shannon entropy is adopted in this study to discern the influence of pre-monsoon thunderstorm on some surface parameters. A few parameters associated with pre-monsoon thunderstorms over a part of east and northeast India are considered. Maximization of Shannon entropy is employed to test the relative contributions of these parameters in creating this weather phenomenon. It follows as a consequence of this information theoretic approach that surface temperature is the most important parameter among those considered. Finally, artificial neural network in the form of multilayer perceptron with backpropagation learning is attempted to develop predictive model for surface temperature.

Keywords

Pre-monsoon thunderstorms Probabilistic information theory Shannon entropy Entropy maximization 

Notes

Acknowledgements

Sincere thanks are due to the anonymous reviewers for their thoughtful suggestions. Financial support from DST, Govt. of India, under Project Grant No. SR/WOS-A/EA-10/2017(G) is thankfully acknowledged by Goutami Chattopadhyay. Authors Surajit Chattopadhyay and Goutami Chattopadhyay are thankful to IUCAA, Pune, India, for the hospitality.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MathematicsAmity UniversityNew Town, KolkataIndia
  2. 2.Department of Atmospheric SciencesUniversity of CalcuttaKolkataIndia

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