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

Abstract 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 artific...

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Autores principales:	Marwah Sattar Hanoon, Ali Najah Ahmed, Nur’atiah Zaini, Arif Razzaq, Pavitra Kumar, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/960c7fe548c048e181298b3277450f28
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spelling	oai:doaj.org-article:960c7fe548c048e181298b3277450f282021-12-02T17:27:19ZDeveloping machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia10.1038/s41598-021-96872-w2045-2322https://doaj.org/article/960c7fe548c048e181298b3277450f282021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96872-whttps://doaj.org/toc/2045-2322Abstract 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.Marwah Sattar HanoonAli Najah AhmedNur’atiah ZainiArif RazzaqPavitra KumarMohsen SherifAhmed SefelnasrAhmed El-ShafieNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marwah Sattar Hanoon Ali Najah Ahmed Nur’atiah Zaini Arif Razzaq Pavitra Kumar Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
description	Abstract 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	Marwah Sattar Hanoon Ali Najah Ahmed Nur’atiah Zaini Arif Razzaq Pavitra Kumar Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie
author_facet	Marwah Sattar Hanoon Ali Najah Ahmed Nur’atiah Zaini Arif Razzaq Pavitra Kumar Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie
author_sort	Marwah Sattar Hanoon
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	https://doaj.org/article/960c7fe548c048e181298b3277450f28
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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

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