Lightning severity classification utilizing the meteorological parameters: a neural network approach
This paper presents a technique of predicting lightning severity on daily basis by using meteorological data. The data used is supplied by Global Lightning Network (GLN) from WSI Corporation. The input of the system consists of seven meteorology parameters which had been provided by Malaysia Meteoro...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68852/ http://psasir.upm.edu.my/id/eprint/68852/1/Lightning%20severity%20classification%20utilizing%20the%20meteorological%20parameters%20a%20neural%20network%20approach.pdf |
| _version_ | 1848856244146667520 |
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| author | Omar, Muhammad Azhar Hassan, Mohd Khair Che Soh, Azura Ab Kadir, Mohd Zainal Abidin |
| author_facet | Omar, Muhammad Azhar Hassan, Mohd Khair Che Soh, Azura Ab Kadir, Mohd Zainal Abidin |
| author_sort | Omar, Muhammad Azhar |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a technique of predicting lightning severity on daily basis by using meteorological data. The data used is supplied by Global Lightning Network (GLN) from WSI Corporation. The input of the system consists of seven meteorology parameters which had been provided by Malaysia Meteorology Service with minimal fees. Input parameters are the Minimum Humidity, Maximum Humidity, Minimum Temperature, Maximum Temperature, Rainfall, Week and Month. The output of the system determines the severity of lightning predictions in three stages; Class1: Hazardous; Class2: Warning; and Class3: Low Risk. Two training algorithms that have been tested in this study namely the Gradient Descent with Momentum Backpropagation (traingdm) and the Scaled Conjugated Gradient Backpropagation (trainscg). The traingdm has indicated better accuracy of 70% compared to the trainscg whilst in contrast; trainscg has demonstrated approximately 4 times faster training compare to traingdm. |
| first_indexed | 2025-11-15T11:38:34Z |
| format | Conference or Workshop Item |
| id | upm-68852 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:38:34Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-688522019-06-11T01:36:39Z http://psasir.upm.edu.my/id/eprint/68852/ Lightning severity classification utilizing the meteorological parameters: a neural network approach Omar, Muhammad Azhar Hassan, Mohd Khair Che Soh, Azura Ab Kadir, Mohd Zainal Abidin This paper presents a technique of predicting lightning severity on daily basis by using meteorological data. The data used is supplied by Global Lightning Network (GLN) from WSI Corporation. The input of the system consists of seven meteorology parameters which had been provided by Malaysia Meteorology Service with minimal fees. Input parameters are the Minimum Humidity, Maximum Humidity, Minimum Temperature, Maximum Temperature, Rainfall, Week and Month. The output of the system determines the severity of lightning predictions in three stages; Class1: Hazardous; Class2: Warning; and Class3: Low Risk. Two training algorithms that have been tested in this study namely the Gradient Descent with Momentum Backpropagation (traingdm) and the Scaled Conjugated Gradient Backpropagation (trainscg). The traingdm has indicated better accuracy of 70% compared to the trainscg whilst in contrast; trainscg has demonstrated approximately 4 times faster training compare to traingdm. IEEE 2013 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68852/1/Lightning%20severity%20classification%20utilizing%20the%20meteorological%20parameters%20a%20neural%20network%20approach.pdf Omar, Muhammad Azhar and Hassan, Mohd Khair and Che Soh, Azura and Ab Kadir, Mohd Zainal Abidin (2013) Lightning severity classification utilizing the meteorological parameters: a neural network approach. In: 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2013), 29 Nov.-1 Dec. 2013, Penang, Malaysia. (pp. 111-116). 10.1109/ICCSCE.2013.6719942 |
| spellingShingle | Omar, Muhammad Azhar Hassan, Mohd Khair Che Soh, Azura Ab Kadir, Mohd Zainal Abidin Lightning severity classification utilizing the meteorological parameters: a neural network approach |
| title | Lightning severity classification utilizing the meteorological parameters: a neural network approach |
| title_full | Lightning severity classification utilizing the meteorological parameters: a neural network approach |
| title_fullStr | Lightning severity classification utilizing the meteorological parameters: a neural network approach |
| title_full_unstemmed | Lightning severity classification utilizing the meteorological parameters: a neural network approach |
| title_short | Lightning severity classification utilizing the meteorological parameters: a neural network approach |
| title_sort | lightning severity classification utilizing the meteorological parameters: a neural network approach |
| url | http://psasir.upm.edu.my/id/eprint/68852/ http://psasir.upm.edu.my/id/eprint/68852/ http://psasir.upm.edu.my/id/eprint/68852/1/Lightning%20severity%20classification%20utilizing%20the%20meteorological%20parameters%20a%20neural%20network%20approach.pdf |