Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring
Cloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunders...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2306 |
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| author | Kamyar Mehranzamir Amin Beiranvand Pour Zulkurnain Abdul-Malek Hadi Nabipour Afrouzi Seyed Morteza Alizadeh Mazlan Hashim |
| author_facet | Kamyar Mehranzamir Amin Beiranvand Pour Zulkurnain Abdul-Malek Hadi Nabipour Afrouzi Seyed Morteza Alizadeh Mazlan Hashim |
| author_sort | Kamyar Mehranzamir |
| collection | DOAJ |
| description | Cloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunderstorm frequencies, such as Malaysia, experience significant fatalities and damage due to lightning strikes. To this end, a lightning locating system (LLS) was developed and deployed in a 400 km<sup>2</sup> study area at the University Technology Malaysia (UTM), Johor, Malaysia for detecting cloud-to-ground lightning discharges. The study utilized a particle swarm optimization (PSO) algorithm as a mediator to identify the best location for a lightning strike. The algorithm was initiated with 30 particles, considering the outcomes of the MDF and TDOA techniques. The effectiveness of the PSO algorithm was found to be dependent on how the search process was arranged. The results of the detected lightning strikes by the PSO-based LLS were compared with an industrial lightning detection system installed in Malaysia. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area was up to 573 m. Therefore, the proposed PSO-based LLS would be efficient and accurate to detect and map the lightning discharges occurring within the coverage area. This study is significant for researchers, insurance companies, and the public seeking to be informed about the impacts of lightning discharges. |
| first_indexed | 2024-03-11T04:08:14Z |
| format | Article |
| id | doaj.art-aff4b6f200fd45ab8966bd775e1af7f6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:14Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-aff4b6f200fd45ab8966bd775e1af7f62023-11-17T23:38:23ZengMDPI AGRemote Sensing2072-42922023-04-01159230610.3390/rs15092306Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and MonitoringKamyar Mehranzamir0Amin Beiranvand Pour1Zulkurnain Abdul-Malek2Hadi Nabipour Afrouzi3Seyed Morteza Alizadeh4Mazlan Hashim5Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor, MalaysiaInstitute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, MalaysiaInstitute of High Voltage & High Current, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaFaculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Kuching 93350, Sarawak, MalaysiaEngineering Institute of Technology, Melbourne, VIC 3000, AustraliaGeoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainable Environment, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaCloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunderstorm frequencies, such as Malaysia, experience significant fatalities and damage due to lightning strikes. To this end, a lightning locating system (LLS) was developed and deployed in a 400 km<sup>2</sup> study area at the University Technology Malaysia (UTM), Johor, Malaysia for detecting cloud-to-ground lightning discharges. The study utilized a particle swarm optimization (PSO) algorithm as a mediator to identify the best location for a lightning strike. The algorithm was initiated with 30 particles, considering the outcomes of the MDF and TDOA techniques. The effectiveness of the PSO algorithm was found to be dependent on how the search process was arranged. The results of the detected lightning strikes by the PSO-based LLS were compared with an industrial lightning detection system installed in Malaysia. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area was up to 573 m. Therefore, the proposed PSO-based LLS would be efficient and accurate to detect and map the lightning discharges occurring within the coverage area. This study is significant for researchers, insurance companies, and the public seeking to be informed about the impacts of lightning discharges.https://www.mdpi.com/2072-4292/15/9/2306lightning locating systemparticle swarm optimizationVLF and VHF sensorsGPS antennaslightning mappingenvironmental monitoring |
| spellingShingle | Kamyar Mehranzamir Amin Beiranvand Pour Zulkurnain Abdul-Malek Hadi Nabipour Afrouzi Seyed Morteza Alizadeh Mazlan Hashim Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring Remote Sensing lightning locating system particle swarm optimization VLF and VHF sensors GPS antennas lightning mapping environmental monitoring |
| title | Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring |
| title_full | Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring |
| title_fullStr | Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring |
| title_full_unstemmed | Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring |
| title_short | Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring |
| title_sort | implementation of ground based lightning locating system using particle swarm optimization algorithm for lightning mapping and monitoring |
| topic | lightning locating system particle swarm optimization VLF and VHF sensors GPS antennas lightning mapping environmental monitoring |
| url | https://www.mdpi.com/2072-4292/15/9/2306 |
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