Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review
This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Published: |
2020
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/92971/ http://dx.doi.org/10.18517/ijaseit.10.1.10163 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.92971 |
---|---|
record_format |
eprints |
spelling |
my.utm.929712021-11-07T05:54:40Z http://eprints.utm.my/id/eprint/92971/ Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review Salleh, N. Yuhaniz, S. S. Sabri, S. F. Mohd. Azmi, N. F. T Technology (General) This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data. 2020 Article PeerReviewed Salleh, N. and Yuhaniz, S. S. and Sabri, S. F. and Mohd. Azmi, N. F. (2020) Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review. International Journal on Advanced Science, Engineering and Information Technology, 10 (1). pp. 9-15. ISSN 2088-5334 http://dx.doi.org/10.18517/ijaseit.10.1.10163 DOI: 10.18517/ijaseit.10.1.10163 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
T Technology (General) |
spellingShingle |
T Technology (General) Salleh, N. Yuhaniz, S. S. Sabri, S. F. Mohd. Azmi, N. F. Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
description |
This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data. |
format |
Article |
author |
Salleh, N. Yuhaniz, S. S. Sabri, S. F. Mohd. Azmi, N. F. |
author_facet |
Salleh, N. Yuhaniz, S. S. Sabri, S. F. Mohd. Azmi, N. F. |
author_sort |
Salleh, N. |
title |
Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
title_short |
Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
title_full |
Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
title_fullStr |
Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
title_full_unstemmed |
Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
title_sort |
enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: a review |
publishDate |
2020 |
url |
http://eprints.utm.my/id/eprint/92971/ http://dx.doi.org/10.18517/ijaseit.10.1.10163 |
_version_ |
1717093400823463936 |
score |
13.209306 |