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Lightning prediction using radiosonde data

This paper presents a concept of predicting lightning with the data from radiosonde using only a simple back propagation neural network model written in C code. The location of interest in this research is Kuala Lumpur International Airport (KLIA). In this model, the parameters related to wind were...

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Bibliographic Details
Main Authors:	Weng L.Y., Omar J.B., Siah Y.K., Abidin I.B.Z., Ahmad S.K.
Other Authors:	26326032700
Format:	Conference paper
Published:	2023
Subjects:	Artificial intelligence Lightning forecasting Lightning prediction Neural networks Electric load forecasting Lightning Radiosondes Back propagation neural networks C codes Kuala lumpur international airports Radiosonde datum Wind parameters
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id	my.uniten.dspace-29717
record_format	dspace
spelling	my.uniten.dspace-297172023-12-28T15:41:46Z Lightning prediction using radiosonde data Weng L.Y. Omar J.B. Siah Y.K. Abidin I.B.Z. Ahmad S.K. 26326032700 24463418200 24448864400 35606640500 25926812900 Artificial intelligence Lightning forecasting Lightning prediction Neural networks Electric load forecasting Lightning Radiosondes Back propagation neural networks C codes Kuala lumpur international airports Radiosonde datum Wind parameters Neural networks This paper presents a concept of predicting lightning with the data from radiosonde using only a simple back propagation neural network model written in C code. The location of interest in this research is Kuala Lumpur International Airport (KLIA). In this model, the parameters related to wind were disregarded. Preliminary results indicate that this method shows some positive results. Future work should include wind parameters to fully capture all properties for lightning formation, subsequently its prediction. Final 2023-12-28T07:41:46Z 2023-12-28T07:41:46Z 2008 Conference paper 2-s2.0-62449251537 https://www.scopus.com/inward/record.uri?eid=2-s2.0-62449251537&partnerID=40&md5=a1367a81aaa94d65392694da7314909f https://irepository.uniten.edu.my/handle/123456789/29717 78 81 Scopus
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/
topic	Artificial intelligence Lightning forecasting Lightning prediction Neural networks Electric load forecasting Lightning Radiosondes Back propagation neural networks C codes Kuala lumpur international airports Radiosonde datum Wind parameters Neural networks
spellingShingle	Artificial intelligence Lightning forecasting Lightning prediction Neural networks Electric load forecasting Lightning Radiosondes Back propagation neural networks C codes Kuala lumpur international airports Radiosonde datum Wind parameters Neural networks Weng L.Y. Omar J.B. Siah Y.K. Abidin I.B.Z. Ahmad S.K. Lightning prediction using radiosonde data
description	This paper presents a concept of predicting lightning with the data from radiosonde using only a simple back propagation neural network model written in C code. The location of interest in this research is Kuala Lumpur International Airport (KLIA). In this model, the parameters related to wind were disregarded. Preliminary results indicate that this method shows some positive results. Future work should include wind parameters to fully capture all properties for lightning formation, subsequently its prediction.
author2	26326032700
author_facet	26326032700 Weng L.Y. Omar J.B. Siah Y.K. Abidin I.B.Z. Ahmad S.K.
format	Conference paper
author	Weng L.Y. Omar J.B. Siah Y.K. Abidin I.B.Z. Ahmad S.K.
author_sort	Weng L.Y.
title	Lightning prediction using radiosonde data
title_short	Lightning prediction using radiosonde data
title_full	Lightning prediction using radiosonde data
title_fullStr	Lightning prediction using radiosonde data
title_full_unstemmed	Lightning prediction using radiosonde data
title_sort	lightning prediction using radiosonde data
publishDate	2023
_version_	1806428036767154176
score	13.222552

Lightning prediction using radiosonde data

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