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.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education (MOE) through Fundamental Research Grant Scheme [FRGS/1/2020/TK0/UNITEN/02/16] |
| Uncontrolled Keywords: | Neural-network; Model; Prediction |
| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 02 Aug 2022 00:00 |
| Last Modified: | 02 Aug 2022 00:00 |
| URI: | http://eprints.um.edu.my/id/eprint/33889 |
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