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Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

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@article{IJASEIT10163,
   author = {Nor’asnilawati Salleh and Siti Sophiayati Yuhaniz and Sharizal Fadlie Sabri and Nurulhuda Firdaus Mohd Azmi},
   title = {Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {1},
   year = {2020},
   pages = {9--15},
   keywords = {prediction; ionosphere; space weather; machine learning; data analytic.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10163},
   doi = {10.18517/ijaseit.10.1.10163}
}

EndNote

%A Salleh, Nor’asnilawati
%A Yuhaniz, Siti Sophiayati
%A Sabri, Sharizal Fadlie
%A Mohd Azmi, Nurulhuda Firdaus
%D 2020
%T Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review
%B 2020
%9 prediction; ionosphere; space weather; machine learning; data analytic.
%! Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review
%K prediction; ionosphere; space weather; machine learning; data analytic.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10163
%R doi:10.18517/ijaseit.10.1.10163
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 1
%@ 2088-5334

IEEE

Nor’asnilawati Salleh,Siti Sophiayati Yuhaniz,Sharizal Fadlie Sabri and Nurulhuda Firdaus Mohd Azmi,"Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 1, pp. 9-15, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.1.10163.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Salleh, Nor’asnilawati
AU  - Yuhaniz, Siti Sophiayati
AU  - Sabri, Sharizal Fadlie
AU  - Mohd Azmi, Nurulhuda Firdaus
PY  - 2020
TI  - Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 1
Y2  - 2020
SP  - 9
EP  - 15
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - prediction; ionosphere; space weather; machine learning; data analytic.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10163
DO  - 10.18517/ijaseit.10.1.10163

RefWorks

RT Journal Article
ID 10163
A1 Salleh, Nor’asnilawati
A1 Yuhaniz, Siti Sophiayati
A1 Sabri, Sharizal Fadlie
A1 Mohd Azmi, Nurulhuda Firdaus
T1 Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 1
YR 2020
SP 9
OP 15
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 prediction; ionosphere; space weather; machine learning; data analytic.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10163
DO  - 10.18517/ijaseit.10.1.10163