Seri Liyana, Ezamzuri and Sarah ‘Atifah, Saruchi and Ammar A., Al-Talib (2025) Comparative analysis of machine learning algorithms for rainfall prediction in Kuantan, Pahang, Malaysia. In: Proceedings of International Conference on Artificial Life and Robotics. 30th International Conference on Artificial Life and Robotics, ICAROB 2025 , 13 - 16 February 2025 , Oita, Japan. 398 -402., 30 (326389). ISSN 2435-9157 ISBN 978-499133372-9 (Published)
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Abstract
This study compares the performance and accuracy of four ML algorithms which are Support Vector Regressor (SVR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), and Linear Regression (LR) in the rainfall prediction application. All four methods employ the same input parameters which are temperature (°c), dew point (°c), humidity (%), wind speed (Kph) and pressure (Hg). Meanwhile the output parameter is set to be the rainfall (mm) which indicates the precipitation in Kuantan, Pahang, Malaysia. The analysis shows that the SVR consistently outperforms the other machine learning algorithms, achieving the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE).
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Artificial Neural Network (ANN); Linear Regressor (LR); Machine Learning (ML); Rainfall prediction; Random Forest Regressor (RFR); Support Vector Regressor (SVR) |
| Subjects: | T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Chemical and Process Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 23 May 2025 01:23 |
| Last Modified: | 23 May 2025 01:23 |
| URI: | http://umpir.ump.edu.my/id/eprint/44631 |
| Download Statistic: | View Download Statistics |
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