Rainfall Prediction in Palembang City Using the GRU and LSTM Methods
Rainfall is one of the weather elements that are very important for the survival of an area. Palembang City, as one of the big cities in Indonesia, is heavily influenced by the level of rainfall that occurs every month. Variations in precipitation can affect various aspects of people's lives,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1730/ http://eprints.intimal.edu.my/1730/1/jods2023_04.pdf |
| _version_ | 1848766818583314432 |
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| author | Surta, Wijaya Tri Basuki, Kurniawan Edi Surya, Negara Yesi Novaria, Kunang |
| author_facet | Surta, Wijaya Tri Basuki, Kurniawan Edi Surya, Negara Yesi Novaria, Kunang |
| author_sort | Surta, Wijaya |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Rainfall is one of the weather elements that are very important for the survival of an area.
Palembang City, as one of the big cities in Indonesia, is heavily influenced by the level of rainfall
that occurs every month. Variations in precipitation can affect various aspects of people's lives,
such as agriculture, industry, tourism, etc. Accurate rainfall predictions can assist in preparing for
multiple activities and making the right decisions. Therefore, it is crucial to research predicting
rainfall in Palembang. It is expected to simplify the prediction process and produce more accurate
results. This research uses the Gated Recurrent Unit (GRU) and Long-Shorted Term Memory
(LSTM) methods to make daily rainfall predictions for the next month using weather element data
for ten (10) years in Palembang, utilizing the deep learning method. The hyperparameter model
tuning experiment was conducted to obtain the best prediction results. From the research results,
it can be concluded that the LSTM model is overall better than the GRU model in predicting daily
rainfall in Palembang City. GRU has RMSE 9.33 and R2 0.54, while the LSTM Model has RMSE
7.45 and R2 0.70. |
| first_indexed | 2025-11-14T11:57:11Z |
| format | Article |
| id | intimal-1730 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:57:11Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-17302023-07-13T08:33:36Z http://eprints.intimal.edu.my/1730/ Rainfall Prediction in Palembang City Using the GRU and LSTM Methods Surta, Wijaya Tri Basuki, Kurniawan Edi Surya, Negara Yesi Novaria, Kunang QA75 Electronic computers. Computer science QA76 Computer software Rainfall is one of the weather elements that are very important for the survival of an area. Palembang City, as one of the big cities in Indonesia, is heavily influenced by the level of rainfall that occurs every month. Variations in precipitation can affect various aspects of people's lives, such as agriculture, industry, tourism, etc. Accurate rainfall predictions can assist in preparing for multiple activities and making the right decisions. Therefore, it is crucial to research predicting rainfall in Palembang. It is expected to simplify the prediction process and produce more accurate results. This research uses the Gated Recurrent Unit (GRU) and Long-Shorted Term Memory (LSTM) methods to make daily rainfall predictions for the next month using weather element data for ten (10) years in Palembang, utilizing the deep learning method. The hyperparameter model tuning experiment was conducted to obtain the best prediction results. From the research results, it can be concluded that the LSTM model is overall better than the GRU model in predicting daily rainfall in Palembang City. GRU has RMSE 9.33 and R2 0.54, while the LSTM Model has RMSE 7.45 and R2 0.70. INTI International University 2023-03-23 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1730/1/jods2023_04.pdf Surta, Wijaya and Tri Basuki, Kurniawan and Edi Surya, Negara and Yesi Novaria, Kunang (2023) Rainfall Prediction in Palembang City Using the GRU and LSTM Methods. Journal of Data Science, 2023 (04). pp. 1-13. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Surta, Wijaya Tri Basuki, Kurniawan Edi Surya, Negara Yesi Novaria, Kunang Rainfall Prediction in Palembang City Using the GRU and LSTM Methods |
| title | Rainfall Prediction in Palembang City Using the GRU and LSTM Methods |
| title_full | Rainfall Prediction in Palembang City Using the GRU and LSTM Methods |
| title_fullStr | Rainfall Prediction in Palembang City Using the GRU and LSTM Methods |
| title_full_unstemmed | Rainfall Prediction in Palembang City Using the GRU and LSTM Methods |
| title_short | Rainfall Prediction in Palembang City Using the GRU and LSTM Methods |
| title_sort | rainfall prediction in palembang city using the gru and lstm methods |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1730/ http://eprints.intimal.edu.my/1730/ http://eprints.intimal.edu.my/1730/1/jods2023_04.pdf |