Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis
Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular...
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| Format: | Article |
| Language: | en |
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2024
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| Online Access: | http://eprints.uthm.edu.my/11801/1/J17378_58cd2d6376f481e780a1563554d90cfa.pdf http://eprints.uthm.edu.my/11801/ https://doi.org/10.54216/JISIoT.110104 |
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| author | Kismiantini, Kismiantini Shaharudin, Shazlyn Milleana Setiawan, Ezra Putranda Urwatul Wutsqa, Dhoriva Ahmad Basri, Muhamad Afdal Mahdin, Hairulnizam A. Mostafa, Salama |
| author_facet | Kismiantini, Kismiantini Shaharudin, Shazlyn Milleana Setiawan, Ezra Putranda Urwatul Wutsqa, Dhoriva Ahmad Basri, Muhamad Afdal Mahdin, Hairulnizam A. Mostafa, Salama |
| author_sort | Kismiantini, Kismiantini |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular spectrum analysis (SSA) is a model-free time series analysis method that is widely used. This study aims to predict the rainfall trends in the Special Region of Yogyakarta, Indonesia, using the Recurrent SSA (SSA-R) and Vector SSA (SSA-V). The SSA-R forecasts using the recurrent continuation directly with the linear recurrent formula, while the SSA-V is a modified recurrent method. This study used 50 years of monthly rainfall data (1970-2019) from 25 stations in the special region of Yogyakarta, Indonesia. The SSA steps for forecasting rainfall data include decomposition (embedding and singular value decomposition), reconstruction (grouping and diagonal averaging), and evaluating the SSA model using w-correlation (if w-correlation is close to zero, returning to the decomposition stage; otherwise, continue the process), forecasting, evaluating the forecast results using root mean square error (RMSE), mean absolute error, r, and mean forecast error, and finally selecting the best model (either the SSA-R or SSA-V model). The results showed that the SSA-R performed better than SSA-V due to the smallest RMSE in the dry, rainy, and inter-monsoon seasons. The SSA-R model’s forecast results revealed faint, constant patterns for the dry, and rainy seasons and an increasing pattern for the inter-monsoon season. The novelty of this study is to compare the performance of the SSA-R and SSA-V models in the large rainfall data in the special region of Yogyakarta, Indonesia. |
| format | Article |
| id | my.uthm.eprints-11801 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | ASPG |
| record_format | eprints |
| spelling | my.uthm.eprints-118012025-04-29T02:57:45Z http://eprints.uthm.edu.my/11801/ Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis Kismiantini, Kismiantini Shaharudin, Shazlyn Milleana Setiawan, Ezra Putranda Urwatul Wutsqa, Dhoriva Ahmad Basri, Muhamad Afdal Mahdin, Hairulnizam A. Mostafa, Salama QC Physics Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular spectrum analysis (SSA) is a model-free time series analysis method that is widely used. This study aims to predict the rainfall trends in the Special Region of Yogyakarta, Indonesia, using the Recurrent SSA (SSA-R) and Vector SSA (SSA-V). The SSA-R forecasts using the recurrent continuation directly with the linear recurrent formula, while the SSA-V is a modified recurrent method. This study used 50 years of monthly rainfall data (1970-2019) from 25 stations in the special region of Yogyakarta, Indonesia. The SSA steps for forecasting rainfall data include decomposition (embedding and singular value decomposition), reconstruction (grouping and diagonal averaging), and evaluating the SSA model using w-correlation (if w-correlation is close to zero, returning to the decomposition stage; otherwise, continue the process), forecasting, evaluating the forecast results using root mean square error (RMSE), mean absolute error, r, and mean forecast error, and finally selecting the best model (either the SSA-R or SSA-V model). The results showed that the SSA-R performed better than SSA-V due to the smallest RMSE in the dry, rainy, and inter-monsoon seasons. The SSA-R model’s forecast results revealed faint, constant patterns for the dry, and rainy seasons and an increasing pattern for the inter-monsoon season. The novelty of this study is to compare the performance of the SSA-R and SSA-V models in the large rainfall data in the special region of Yogyakarta, Indonesia. ASPG 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11801/1/J17378_58cd2d6376f481e780a1563554d90cfa.pdf Kismiantini, Kismiantini and Shaharudin, Shazlyn Milleana and Setiawan, Ezra Putranda and Urwatul Wutsqa, Dhoriva and Ahmad Basri, Muhamad Afdal and Mahdin, Hairulnizam and A. Mostafa, Salama (2024) Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems and Internet of Things, 11 (1). pp. 29-43. https://doi.org/10.54216/JISIoT.110104 |
| spellingShingle | QC Physics Kismiantini, Kismiantini Shaharudin, Shazlyn Milleana Setiawan, Ezra Putranda Urwatul Wutsqa, Dhoriva Ahmad Basri, Muhamad Afdal Mahdin, Hairulnizam A. Mostafa, Salama Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis |
| title | Prediction of Rainfall Trends Using Forecasting Approaches
Based on Singular Spectrum Analysis |
| title_full | Prediction of Rainfall Trends Using Forecasting Approaches
Based on Singular Spectrum Analysis |
| title_fullStr | Prediction of Rainfall Trends Using Forecasting Approaches
Based on Singular Spectrum Analysis |
| title_full_unstemmed | Prediction of Rainfall Trends Using Forecasting Approaches
Based on Singular Spectrum Analysis |
| title_short | Prediction of Rainfall Trends Using Forecasting Approaches
Based on Singular Spectrum Analysis |
| title_sort | prediction of rainfall trends using forecasting approaches
based on singular spectrum analysis |
| topic | QC Physics |
| url | http://eprints.uthm.edu.my/11801/1/J17378_58cd2d6376f481e780a1563554d90cfa.pdf http://eprints.uthm.edu.my/11801/ https://doi.org/10.54216/JISIoT.110104 |
| url_provider | http://eprints.uthm.edu.my/ |