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Lightning forecasting using ANN-BP & radiosonde

This paper presents a concept of predicting lightning with the data from radiosonde and a more reliable dataset of lightning occurances from Tenaga Nasionl Berhad Research Sdn Berhad (TNBR). The location of interest in this research is Kuala Lumpur International Airport (KLIA). The engine used for p...

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Main Authors: Weng, L.Y., Omar, J.B., Siah, Y.K., Ahmed, S.K., Abidin, I.B.Z., Abdullah, N.
Format: Conference Paper
Language:English
Published: 2017
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/6300
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spelling my.uniten.dspace-50242018-12-13T01:04:43Z Lightning forecasting using ANN-BP & radiosonde Weng, L.Y. Omar, J.B. Siah, Y.K. Ahmed, S.K. Abidin, I.B.Z. Abdullah, N. This paper presents a concept of predicting lightning with the data from radiosonde and a more reliable dataset of lightning occurances from Tenaga Nasionl Berhad Research Sdn Berhad (TNBR). The location of interest in this research is Kuala Lumpur International Airport (KLIA). The engine used for prediction is of a Neural Network Back Propogation type (ANN-BP). The initial results show that the combination of datasets and engine are workable, however the prediction results seem to be more biased towards lightning days as compared to non-lightning days. © 2010 IEEE. 2017-11-14T03:21:27Z 2017-11-14T03:21:27Z 2010 Conference Paper http://dspace.uniten.edu.my/jspui/handle/123456789/6300 10.1109/ICICCI.2010.83 en Proceedings - 2010 International Conference on Intelligent Computing and Cognitive Informatics, ICICCI 2010 2010, Article number 5566013, Pages 152-155
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description This paper presents a concept of predicting lightning with the data from radiosonde and a more reliable dataset of lightning occurances from Tenaga Nasionl Berhad Research Sdn Berhad (TNBR). The location of interest in this research is Kuala Lumpur International Airport (KLIA). The engine used for prediction is of a Neural Network Back Propogation type (ANN-BP). The initial results show that the combination of datasets and engine are workable, however the prediction results seem to be more biased towards lightning days as compared to non-lightning days. © 2010 IEEE.
format Conference Paper
author Weng, L.Y.
Omar, J.B.
Siah, Y.K.
Ahmed, S.K.
Abidin, I.B.Z.
Abdullah, N.
spellingShingle Weng, L.Y.
Omar, J.B.
Siah, Y.K.
Ahmed, S.K.
Abidin, I.B.Z.
Abdullah, N.
Lightning forecasting using ANN-BP & radiosonde
author_facet Weng, L.Y.
Omar, J.B.
Siah, Y.K.
Ahmed, S.K.
Abidin, I.B.Z.
Abdullah, N.
author_sort Weng, L.Y.
title Lightning forecasting using ANN-BP & radiosonde
title_short Lightning forecasting using ANN-BP & radiosonde
title_full Lightning forecasting using ANN-BP & radiosonde
title_fullStr Lightning forecasting using ANN-BP & radiosonde
title_full_unstemmed Lightning forecasting using ANN-BP & radiosonde
title_sort lightning forecasting using ann-bp & radiosonde
publishDate 2017
url http://dspace.uniten.edu.my/jspui/handle/123456789/6300
_version_ 1644493593176440832
score 13.15806

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