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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neura...

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Main Authors: Hanoon, Marwah Sattar, Ahmed, Ali Najah, Zaini, Nur'atiah, Razzaq, Arif, Kumar, Pavitra, Sherif, Mohsen, Sefelnasr, Ahmed, Ahmed El-Shafie, Ahmed Hussein Kamel
Format: Article
Published: Nature Portfolio 2021
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.um.edu.my/33889/
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spelling my.um.eprints.338892022-08-02T00:00:46Z http://eprints.um.edu.my/33889/ Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia Hanoon, Marwah Sattar Ahmed, Ali Najah Zaini, Nur'atiah Razzaq, Arif Kumar, Pavitra Sherif, Mohsen Sefelnasr, Ahmed Ahmed El-Shafie, Ahmed Hussein Kamel Q Science (General) T Technology (General) Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy. Nature Portfolio 2021-09-23 Article PeerReviewed Hanoon, Marwah Sattar and Ahmed, Ali Najah and Zaini, Nur'atiah and Razzaq, Arif and Kumar, Pavitra and Sherif, Mohsen and Sefelnasr, Ahmed and Ahmed El-Shafie, Ahmed Hussein Kamel (2021) Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia. Scientific Reports, 11 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-021-96872-w <https://doi.org/10.1038/s41598-021-96872-w>. 10.1038/s41598-021-96872-w
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Hanoon, Marwah Sattar
Ahmed, Ali Najah
Zaini, Nur'atiah
Razzaq, Arif
Kumar, Pavitra
Sherif, Mohsen
Sefelnasr, Ahmed
Ahmed El-Shafie, Ahmed Hussein Kamel
Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
description Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.
format Article
author Hanoon, Marwah Sattar
Ahmed, Ali Najah
Zaini, Nur'atiah
Razzaq, Arif
Kumar, Pavitra
Sherif, Mohsen
Sefelnasr, Ahmed
Ahmed El-Shafie, Ahmed Hussein Kamel
author_facet Hanoon, Marwah Sattar
Ahmed, Ali Najah
Zaini, Nur'atiah
Razzaq, Arif
Kumar, Pavitra
Sherif, Mohsen
Sefelnasr, Ahmed
Ahmed El-Shafie, Ahmed Hussein Kamel
author_sort Hanoon, Marwah Sattar
title Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
title_short Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
title_full Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
title_fullStr Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
title_full_unstemmed Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
title_sort developing machine learning algorithms for meteorological temperature and humidity forecasting at terengganu state in malaysia
publisher Nature Portfolio
publishDate 2021
url http://eprints.um.edu.my/33889/
_version_ 1740826048344358912
score 13.211869

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