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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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CNKI
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etc
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Acceptance Rate:
19%
APC:
500 USD
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Impact Factor 2022: 1.0
3.1
2022
CiteScore
49th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
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2020
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Volume 11, No. 2, May 2020
>
Meteorology Visibility Estimation by Using Multi-Support Vector Regression Method
Wai Lun Lo, Meimei Zhu, and Hong Fu
Department of Computer Science, Chu Hai College of Higher Education, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, N.T. Hong Kong
Abstract
—Meteorological visibility measures the transparency of the atmosphere or air and it provides important information for road, flight and sea transportation safety. Problem of pollution can also affect the visibility of a certain area. Measurement and estimation of visibility is a challenging and complex problem as visibility is affected by various factors such as dust, smoke, fog and haze. Traditional digital image-based approach for visibility estimation involve applications of the meteorology law and mathematical analysis. Digital image-based and machine learning approach can be one of the solutions to this complex problem. In this paper, we propose an intelligent digital method for visibility estimation. Effective regions are first extracted from the digital images and then classified into different classes by using Support Vector Machines (SVM). Multi-Supported Vector Regression (MSVR) models are used to predict the meteorological visibility by using the image features values generated by VGG Neural Network. SVR machine learning method is used for model training and the resulting system can be used for meteorological visibility estimation.
Index Terms
—meteorology visibility, weather photo, deep learning, feature extraction, support vector regression
Cite: Wai Lun Lo, Meimei Zhu, and Hong Fu, "Meteorology Visibility Estimation by Using Multi-Support Vector Regression Method," Journal of Advances in Information Technology, Vol. 11, No. 2, pp. 40-47, May 2020. doi: 10.12720/jait.11.2.40-47
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
1-A3005-Hong Kong
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