Cover Image

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

air temperature; article; forecasting; linear regression analysis; multilayer perceptron; prediction; radial basis function; random forest; relative humidity; Terengganu

Saved in:
Bibliographic Details
Main Authors: Hanoon M.S., Ahmed A.N., Zaini N., Razzaq A., Kumar P., Sherif M., Sefelnasr A., El-Shafie A.
Other Authors: 57266877500
Format: Article
Published: Nature Research 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-25879
record_format dspace
spelling my.uniten.dspace-258792023-05-29T17:05:23Z Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia Hanoon M.S. Ahmed A.N. Zaini N. Razzaq A. Kumar P. Sherif M. Sefelnasr A. El-Shafie A. 57266877500 57214837520 56905328500 57219410567 57206939156 7005414714 6505592467 16068189400 air temperature; article; forecasting; linear regression analysis; multilayer perceptron; prediction; radial basis function; random forest; relative humidity; Terengganu 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. � 2021, The Author(s). Final 2023-05-29T09:05:23Z 2023-05-29T09:05:23Z 2021 Article 10.1038/s41598-021-96872-w 2-s2.0-85115394454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115394454&doi=10.1038%2fs41598-021-96872-w&partnerID=40&md5=f9996fb6bc6f396520cbf6eb411a9acc https://irepository.uniten.edu.my/handle/123456789/25879 11 1 18935 All Open Access, Gold, Green Nature Research Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description air temperature; article; forecasting; linear regression analysis; multilayer perceptron; prediction; radial basis function; random forest; relative humidity; Terengganu
author2 57266877500
author_facet 57266877500
Hanoon M.S.
Ahmed A.N.
Zaini N.
Razzaq A.
Kumar P.
Sherif M.
Sefelnasr A.
El-Shafie A.
format Article
author Hanoon M.S.
Ahmed A.N.
Zaini N.
Razzaq A.
Kumar P.
Sherif M.
Sefelnasr A.
El-Shafie A.
spellingShingle Hanoon M.S.
Ahmed A.N.
Zaini N.
Razzaq A.
Kumar P.
Sherif M.
Sefelnasr A.
El-Shafie A.
Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia
author_sort Hanoon M.S.
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 Research
publishDate 2023
_version_ 1806423355095515136
score 13.222552

Similar Items