| Title: | OS13-4 Comparative Analysis of Machine Learning Algorithms for Rainfall Prediction in Kuantan, Pahang, Malaysia. |
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| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 398-402 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS13-4 |
| Author(s): | Seri Liyana Ezamzuri, Sarah Atifah Saruchi, Ammar A Al-Talib |
| Publication Date: | February 13, 2025 |
| Keywords: | Machine Learning (ML), Support Vector Regressor (SVR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), Linear Regressor (LR), Rainfall prediction |
| 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). |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS13/OS13-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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