Long Short-Term Memory as a Rainfall Forecasting Model for Bogor City in 2025-2026

Long Short-Term Memory as a Rainfall Forecasting Model for Bogor City in 2025-2026

Indonesia is a country with a tropical climate that has unique and changing weather patterns. Accurate rainfall prediction can help local governments, farmers, and the broader community plan activities that depend on rainfall patterns. This research aims to develop a rainfall prediction model for Bo...

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Main Authors: Nur Anggraini Fadhilah, Muhammad Abshor Dzulhij Rizki, Muhammad Ryan Azahran, Siti Arbaynah, Rakesha Putra Antique Yusuf, Yenni Angraini, Muhammad Rizky Nurhambali
Format: Article
Language:English
Published: Politeknik Negeri Batam 2025-03-01
Series:Journal of Applied Informatics and Computing
Subjects:
cross-validation
forecasting
lstm
rainfall
Online Access:https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9068
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Summary:Indonesia is a country with a tropical climate that has unique and changing weather patterns. Accurate rainfall prediction can help local governments, farmers, and the broader community plan activities that depend on rainfall patterns. This research aims to develop a rainfall prediction model for Bogor City using past rainfall data in Bogor City, which is known as an area with high rainfall levels and dynamic rainfall patterns. The analysis utilizes rainfall data recorded by the JAXA satellite from January 1, 2014, to December 31, 2024. The prediction method implemented in this research is the long short-term memory (LSTM). The LSTM modelling process evaluates various models by comparing RMSE, MAE, and correlation values through expanding window cross-validation, selecting the model with the lowest average RMSE and MAE with the highest correlation as the optimal choice. The best-performing model was achieved with 25 epochs and a batch size of 1, resulting in an average RMSE of 56.3340, MAE of 35.5223, and correlation of 0.3209. This best-performing model is then employed to predict rainfall for the next two years. The results show significant daily variations in the predicted rainfall but can capture existing seasonal patterns.
ISSN:2548-6861

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