Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport
The Sultan Mahmud Badaruddin (SMB) II Palembang Meteorological Station is a technical implementation unit (UPT) of the Meteorology, Climatology, and Geophysics Agency (BMKG) that plays a role in disseminating actual weather information, particularly at SMBII Palembang Airport. Various wea...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2024
|
| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1955/ http://eprints.intimal.edu.my/1955/1/497 |
| _version_ | 1848766881456979968 |
|---|---|
| author | Akbar Rizki, Ramadhan Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara |
| author_facet | Akbar Rizki, Ramadhan Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara |
| author_sort | Akbar Rizki, Ramadhan |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The Sultan Mahmud Badaruddin (SMB) II Palembang Meteorological Station is a technical implementation unit (UPT) of the Meteorology, Climatology, and Geophysics Agency (BMKG) that plays a role in disseminating actual weather information, particularly at SMBII Palembang Airport. Various weather parameters are observed, one of which is wind speed. During the take-off and landing processes, wind speed is a crucial parameter used by airport personnel, including pilots and air traffic controllers (ATC). This study focuses on analyzingand evaluating three deep learning methods using the architectures of LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), and BiLSTM (Bidirectional Long Short Term Memory). Time series data such as air pressure, rainfall, humidity, and temperature are used as predictors. The data is sourced from the AWOS (Automatic Weather Observation System) device. After processing the data using deep learning methods with the architectures above, an analysis will be conducted to determine which architecture model is the most accurate based on the lowest loss error rate in forecasting wind speed at SMB II Palembang Airport. The results show that the GRU deep learning architecture has the lowest loss value compared to the LSTM and BiLSTM architectures so that it can produce better wind speed forecasts in the next 12 hours and 24 hours, with RMSE of 1.62 and 1.77, respectively. |
| first_indexed | 2025-11-14T11:58:11Z |
| format | Article |
| id | intimal-1955 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:11Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-19552024-08-02T03:24:18Z http://eprints.intimal.edu.my/1955/ Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport Akbar Rizki, Ramadhan Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software The Sultan Mahmud Badaruddin (SMB) II Palembang Meteorological Station is a technical implementation unit (UPT) of the Meteorology, Climatology, and Geophysics Agency (BMKG) that plays a role in disseminating actual weather information, particularly at SMBII Palembang Airport. Various weather parameters are observed, one of which is wind speed. During the take-off and landing processes, wind speed is a crucial parameter used by airport personnel, including pilots and air traffic controllers (ATC). This study focuses on analyzingand evaluating three deep learning methods using the architectures of LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), and BiLSTM (Bidirectional Long Short Term Memory). Time series data such as air pressure, rainfall, humidity, and temperature are used as predictors. The data is sourced from the AWOS (Automatic Weather Observation System) device. After processing the data using deep learning methods with the architectures above, an analysis will be conducted to determine which architecture model is the most accurate based on the lowest loss error rate in forecasting wind speed at SMB II Palembang Airport. The results show that the GRU deep learning architecture has the lowest loss value compared to the LSTM and BiLSTM architectures so that it can produce better wind speed forecasts in the next 12 hours and 24 hours, with RMSE of 1.62 and 1.77, respectively. INTI International University 2024-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1955/1/497 Akbar Rizki, Ramadhan and Tri Basuki, Kurniawan and Misinem, . and Muhammad Izman, Herdiansyah and Edi Surya, Negara (2024) Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport. Journal of Data Science, 2024 (23). pp. 1-11. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Akbar Rizki, Ramadhan Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport |
| title | Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport |
| title_full | Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport |
| title_fullStr | Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport |
| title_full_unstemmed | Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport |
| title_short | Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport |
| title_sort | deep learning techniques for wind speed forecasting at palembang airport |
| topic | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1955/ http://eprints.intimal.edu.my/1955/ http://eprints.intimal.edu.my/1955/1/497 |