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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,...

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Main Authors:	Surta, Wijaya, Tri Basuki, Kurniawan, Edi Surya, Negara, Yesi Novaria, Kunang
Format:	Article
Language:	English
Published:	INTI International University 2023
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://eprints.intimal.edu.my/1730/1/jods2023_04.pdf http://eprints.intimal.edu.my/1730/ http://ipublishing.intimal.edu.my/jods.html
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id	my-inti-eprints.1730
record_format	eprints
spelling	my-inti-eprints.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
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
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
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.
format	Article
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
title	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_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_sort	rainfall prediction in palembang city using the gru and lstm methods
publisher	INTI International University
publishDate	2023
url	http://eprints.intimal.edu.my/1730/1/jods2023_04.pdf http://eprints.intimal.edu.my/1730/ http://ipublishing.intimal.edu.my/jods.html
_version_	1772816846022508544
score	13.211869

Rainfall Prediction in Palembang City Using the GRU and LSTM Methods

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