Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport
In the context of aviation, the anticipation of visibility is contingent upon the consideration of diverse meteorological factors. This study systematically examines the influence of the cross-validation technique (k) on the precision of visibility predictions, as gauged by root mean square error an...
Published in: | New Trends in Civil Aviation |
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Czech Technical University in Prague
2024
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193038618&doi=10.23919%2fNTCA60572.2024.10517824&partnerID=40&md5=c54f4f5104a40a66a2f557f90392d197 |
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2-s2.0-85193038618 Bin Jamaludin W.M.R.; Wan Mohamed W.M.B.; Bin Nik Ali N.H.; Binti Mohd Isa N.A. Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport 2024 New Trends in Civil Aviation 10.23919/NTCA60572.2024.10517824 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193038618&doi=10.23919%2fNTCA60572.2024.10517824&partnerID=40&md5=c54f4f5104a40a66a2f557f90392d197 In the context of aviation, the anticipation of visibility is contingent upon the consideration of diverse meteorological factors. This study systematically examines the influence of the cross-validation technique (k) on the precision of visibility predictions, as gauged by root mean square error and mean absolute error. Employing the Regression Learner, encompassing 26 predetermined algorithms, and employing cross-validation (k) iterations ranging from 5 to 15, the primary objective was to discern the optimal model for visibility prognosis. Notably, our analysis extends to two distinct airports in Peninsular Malaysia, thereby enabling a comparative assessment. Results elucidate that the Gaussian Process Regression model consistently demonstrates superior efficacy across varied meteorological parameters and diverse k values. The outcomes of this study are poised to yield practical implications, particularly in refining visibility prognostications and mitigating the likelihood of aviation incidents. © 2024 Czech Technical University in Prague. Czech Technical University in Prague 26947854 English Conference paper |
author |
Bin Jamaludin W.M.R.; Wan Mohamed W.M.B.; Bin Nik Ali N.H.; Binti Mohd Isa N.A. |
spellingShingle |
Bin Jamaludin W.M.R.; Wan Mohamed W.M.B.; Bin Nik Ali N.H.; Binti Mohd Isa N.A. Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
author_facet |
Bin Jamaludin W.M.R.; Wan Mohamed W.M.B.; Bin Nik Ali N.H.; Binti Mohd Isa N.A. |
author_sort |
Bin Jamaludin W.M.R.; Wan Mohamed W.M.B.; Bin Nik Ali N.H.; Binti Mohd Isa N.A. |
title |
Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
title_short |
Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
title_full |
Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
title_fullStr |
Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
title_full_unstemmed |
Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
title_sort |
Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport |
publishDate |
2024 |
container_title |
New Trends in Civil Aviation |
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container_issue |
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doi_str_mv |
10.23919/NTCA60572.2024.10517824 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193038618&doi=10.23919%2fNTCA60572.2024.10517824&partnerID=40&md5=c54f4f5104a40a66a2f557f90392d197 |
description |
In the context of aviation, the anticipation of visibility is contingent upon the consideration of diverse meteorological factors. This study systematically examines the influence of the cross-validation technique (k) on the precision of visibility predictions, as gauged by root mean square error and mean absolute error. Employing the Regression Learner, encompassing 26 predetermined algorithms, and employing cross-validation (k) iterations ranging from 5 to 15, the primary objective was to discern the optimal model for visibility prognosis. Notably, our analysis extends to two distinct airports in Peninsular Malaysia, thereby enabling a comparative assessment. Results elucidate that the Gaussian Process Regression model consistently demonstrates superior efficacy across varied meteorological parameters and diverse k values. The outcomes of this study are poised to yield practical implications, particularly in refining visibility prognostications and mitigating the likelihood of aviation incidents. © 2024 Czech Technical University in Prague. |
publisher |
Czech Technical University in Prague |
issn |
26947854 |
language |
English |
format |
Conference paper |
accesstype |
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record_format |
scopus |
collection |
Scopus |
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1809678013628940288 |