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Paper Title: Development of Artificial Neural Networks for severe weather system
Authors Name: Y.Md.Riyazuddin , Dr S Mahaboob Basha , Dr K Krishna Reddy
Unique Id: IJRTI1707034
Published In: Volume 2 Issue 7, July-2017
Abstract: Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics. Accurate forecasting of severe thunderstorms is critical for a large range of users in the community. In this paper, experiments are conducted with artificial neural network model to predict severe thunderstorms that occurred over Kolkata during May 3, 11, and 15, 2009, using thunderstorm affected meteorological parameters. The capabilities of six learning algorithms, namely, Step, Momentum, Conjugate Gradient, Quick Propagation, Levenberg-Marquardt, and Delta-Bar-Delta, in predicting thunderstorms and the usefulness for the advanced prediction were studied and their performances were evaluated by a number of statistical measures. The results indicate that Levenberg-Marquardt algorithm well predicted thunderstorm affected surface parameters and 1, 3, and 24 h advanced prediction models are able to predict hourly temperature and relative humidity adequately with sudden fall and rise during thunderstorm hour. This demonstrates its distinct capability and advantages in identifying meteorological time series comprising nonlinear characteristics. The developed model can be useful in decision making for meteorologists and others who work with real-time thunderstorm forecast.
Keywords: ANN- Artificial Neural Networks
Cite Article: "Development of Artificial Neural Networks for severe weather system", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 7, page no.220 - 230, July-2017, Available :http://www.ijrti.org/papers/IJRTI1707034.pdf
Downloads: 00048
Publication Details: Published Paper ID: IJRTI1707034
Registration ID:170440
Published In: Volume 2 Issue 7, July-2017
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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