High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor
Due to the discrepancy in resolution between existing global climate model output and the resolution required by decisionmakers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning appro...
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2024
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| Online Access: | http://eprints.uthm.edu.my/12511/1/J17997_ecd1f1d676ce02fa7bba152d8ffcc248.pdf http://eprints.uthm.edu.my/12511/ |
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| author | Abdul Rashid, Raghdah Rasyidah Shaharudin, Shazlyn Milleana Sulaiman, Nurul Ainina Filza Zainuddin, Nurul Hila Mahdin, Hairulnizam Mohd Najib, Summayah Aimi Hidayat, Rahmat |
| author_facet | Abdul Rashid, Raghdah Rasyidah Shaharudin, Shazlyn Milleana Sulaiman, Nurul Ainina Filza Zainuddin, Nurul Hila Mahdin, Hairulnizam Mohd Najib, Summayah Aimi Hidayat, Rahmat |
| author_sort | Abdul Rashid, Raghdah Rasyidah |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Due to the discrepancy in resolution between existing global climate model output and the resolution required by decisionmakers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors. |
| format | Article |
| id | my.uthm.eprints-12511 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | joiv |
| record_format | eprints |
| spelling | my.uthm.eprints-125112025-05-05T06:35:45Z http://eprints.uthm.edu.my/12511/ High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor Abdul Rashid, Raghdah Rasyidah Shaharudin, Shazlyn Milleana Sulaiman, Nurul Ainina Filza Zainuddin, Nurul Hila Mahdin, Hairulnizam Mohd Najib, Summayah Aimi Hidayat, Rahmat QC Physics Due to the discrepancy in resolution between existing global climate model output and the resolution required by decisionmakers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors. joiv 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12511/1/J17997_ecd1f1d676ce02fa7bba152d8ffcc248.pdf Abdul Rashid, Raghdah Rasyidah and Shaharudin, Shazlyn Milleana and Sulaiman, Nurul Ainina Filza and Zainuddin, Nurul Hila and Mahdin, Hairulnizam and Mohd Najib, Summayah Aimi and Hidayat, Rahmat (2024) High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor. INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, 8 (2). pp. 692-699. |
| spellingShingle | QC Physics Abdul Rashid, Raghdah Rasyidah Shaharudin, Shazlyn Milleana Sulaiman, Nurul Ainina Filza Zainuddin, Nurul Hila Mahdin, Hairulnizam Mohd Najib, Summayah Aimi Hidayat, Rahmat High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor |
| title | High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor |
| title_full | High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor |
| title_fullStr | High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor |
| title_full_unstemmed | High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor |
| title_short | High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor |
| title_sort | high-resolution downscaling with interpretable relevant vector machine: rainfall prediction for case study in selangor |
| topic | QC Physics |
| url | http://eprints.uthm.edu.my/12511/1/J17997_ecd1f1d676ce02fa7bba152d8ffcc248.pdf http://eprints.uthm.edu.my/12511/ |
| url_provider | http://eprints.uthm.edu.my/ |