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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.
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Published: 2019
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/11413
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spelling my.uniten.dspace-114132019-01-02T06:41:05Z 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. 2019-01-02T06:41:05Z 2019-01-02T06:41:05Z 2010 http://dspace.uniten.edu.my/jspui/handle/123456789/11413
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/
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.
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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 2019
url http://dspace.uniten.edu.my/jspui/handle/123456789/11413
_version_ 1644495203517595648
score 13.15806

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