Comparative analysis of machine learning algorithms for rainfall prediction in Kuantan, Pahang, Malaysia
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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
ALife Robotics Corporation Ltd
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44631/ http://umpir.ump.edu.my/id/eprint/44631/1/Comparative%20Analaysis%20of%20Machine%20Learning%20Algorithms%20for%20Rainfall%20Prediction.pdf |
| Summary: | 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). |
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