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An application of deep learning for lightning prediction in East Coast Malaysia

This paper presents the application of deep learning (DL) approach namely Feed-Forward Neural Networks (FFNN) in predicting the location of lightning occurrences within 100 km radius from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan, Pahang Malaysia. The recorded data were obtained fr...

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Main Authors: Mohd Herwan, Sulaiman, Amir Izzani, Mohamed, Zuriani, Mustaffa
Format: Article
Language:English
Published: Elsevier Ltd 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39701/1/An%20application%20of%20deep%20learning%20for%20lightning%20prediction%20in%20East%20Coast%20Malaysia.pdf
http://umpir.ump.edu.my/id/eprint/39701/
https://doi.org/10.1016/j.prime.2023.100340
https://doi.org/10.1016/j.prime.2023.100340
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spelling my.ump.umpir.397012023-12-20T08:14:41Z http://umpir.ump.edu.my/id/eprint/39701/ An application of deep learning for lightning prediction in East Coast Malaysia Mohd Herwan, Sulaiman Amir Izzani, Mohamed Zuriani, Mustaffa TK Electrical engineering. Electronics Nuclear engineering This paper presents the application of deep learning (DL) approach namely Feed-Forward Neural Networks (FFNN) in predicting the location of lightning occurrences within 100 km radius from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan, Pahang Malaysia. The recorded data were obtained from Malaysia Meteorology Department (MET Malaysia), where the inputs of the DL are the intensity of the lightning in kilo Ampere, direction in degrees, distance and major axis that measures in km, while the output is the latitude and longitude of the lightning occurrences. The data are divided into training, validation and testing to measure the performance of the developed DL model. The findings of the study demonstrated the promising results of FFNN in terms of obtaining the minimum error which significantly increasing the accuracy of the predictions. To show the effectiveness of FFNN, the comparison study has been conducted with Long Short-Term Memory (LSTM) networks. From the simulation, it can be seen that FFNN can be used as an effective tool for predicting the location of lightning occurred better than the LSTM. Elsevier Ltd 2023 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/39701/1/An%20application%20of%20deep%20learning%20for%20lightning%20prediction%20in%20East%20Coast%20Malaysia.pdf Mohd Herwan, Sulaiman and Amir Izzani, Mohamed and Zuriani, Mustaffa (2023) An application of deep learning for lightning prediction in East Coast Malaysia. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 6 (100340). pp. 1-12. ISSN 2772-6711. (Published) https://doi.org/10.1016/j.prime.2023.100340 https://doi.org/10.1016/j.prime.2023.100340
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mohd Herwan, Sulaiman
Amir Izzani, Mohamed
Zuriani, Mustaffa
An application of deep learning for lightning prediction in East Coast Malaysia
description This paper presents the application of deep learning (DL) approach namely Feed-Forward Neural Networks (FFNN) in predicting the location of lightning occurrences within 100 km radius from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan, Pahang Malaysia. The recorded data were obtained from Malaysia Meteorology Department (MET Malaysia), where the inputs of the DL are the intensity of the lightning in kilo Ampere, direction in degrees, distance and major axis that measures in km, while the output is the latitude and longitude of the lightning occurrences. The data are divided into training, validation and testing to measure the performance of the developed DL model. The findings of the study demonstrated the promising results of FFNN in terms of obtaining the minimum error which significantly increasing the accuracy of the predictions. To show the effectiveness of FFNN, the comparison study has been conducted with Long Short-Term Memory (LSTM) networks. From the simulation, it can be seen that FFNN can be used as an effective tool for predicting the location of lightning occurred better than the LSTM.
format Article
author Mohd Herwan, Sulaiman
Amir Izzani, Mohamed
Zuriani, Mustaffa
author_facet Mohd Herwan, Sulaiman
Amir Izzani, Mohamed
Zuriani, Mustaffa
author_sort Mohd Herwan, Sulaiman
title An application of deep learning for lightning prediction in East Coast Malaysia
title_short An application of deep learning for lightning prediction in East Coast Malaysia
title_full An application of deep learning for lightning prediction in East Coast Malaysia
title_fullStr An application of deep learning for lightning prediction in East Coast Malaysia
title_full_unstemmed An application of deep learning for lightning prediction in East Coast Malaysia
title_sort application of deep learning for lightning prediction in east coast malaysia
publisher Elsevier Ltd
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/39701/1/An%20application%20of%20deep%20learning%20for%20lightning%20prediction%20in%20East%20Coast%20Malaysia.pdf
http://umpir.ump.edu.my/id/eprint/39701/
https://doi.org/10.1016/j.prime.2023.100340
https://doi.org/10.1016/j.prime.2023.100340
_version_ 1822923998596431872
score 13.23243

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