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

Authors

  • Nur Anggraini Fadhilah Statistika dan Sains Data, Institut Pertanian Bogor
  • Muhammad Abshor Dzulhij Rizki Statistika dan Sains Data, Institut Pertanian Bogor
  • Muhammad Ryan Azahran Statistika dan Sains Data, Institut Pertanian Bogor
  • Siti Arbaynah Statistika dan Sains Data, Institut Pertanian Bogor
  • Rakesha Putra Antique Yusuf Statistika dan Sains Data, Institut Pertanian Bogor
  • Yenni Angraini Statistika dan Sains Data, Institut Pertanian Bogor
  • Muhammad Rizky Nurhambali Statistika dan Sains Data, Institut Pertanian Bogor

DOI:

https://doi.org/10.30871/jaic.v9i2.9068

Keywords:

Cross-validation, Forecasting, LSTM, Rainfall

Abstract

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.

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References

[1] K. Yamamoto, T. Sayama, and Apip, “Impact of climate change on flood inundation in a tropical river basin in Indonesia,” Progress in Earth and Planetary Science, vol. 8, no. 1, p. 5, Jan. 2021, doi: 10.1186/s40645-020-00386-4.

[2] E. S. Buffa and R. K. Sarin, Modem Production/ Operations Management. Los Angeles, CA, USA: John Wiley & Sons, Inc., 1987.

[3] J. Patterson and A. Gibson, Deep Learning A Practitioners Approach. California (US): O’Reilly Media, 2017.

[4] O. A. Qasem, M. Akour, and M. Alenezi, “The Influence of Deep Learning Algorithms Factors in Software Fault Prediction,” IEEE Access, vol. 8, pp. 63945–63960, 2020, doi: 10.1109/ACCESS.2020.2985290.

[5] J. N. van Rijn and F. Hutter, “Hyperparameter Importance Across Datasets,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD ’18. New York, NY, USA: Association for Computing Machinery, Jul. 2018, pp. 2367–2376. doi: 10.1145/3219819.3220058.

[6] K. Cho and Y. Kim, “Improving streamflow prediction in the WRF-Hydro model with LSTM networks,” Journal of Hydrology, vol. 605, p. 127297, Feb. 2022, doi: 10.1016/j.jhydrol.2021.127297.

[7] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.

[8] A. Chaudhary, K. S. Chouhan, J. Gajrani, and B. Sharma, “Deep Learning With PyTorch,” in Machine Learning and Deep Learning in Real-Time Applications, IGI Global Scientific Publishing, 2020, pp. 61–95. doi: 10.4018/978-1-7998-3095-5.ch003.

[9] H. Sak, A. Senior, and F. Beaufays, “Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition,” Feb. 05, 2014, arXiv: arXiv:1402.1128. doi: 10.48550/arXiv.1402.1128.

[10] P. Le and W. Zuidema, “Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs,” Mar. 01, 2016, arXiv: arXiv:1603.00423. doi: 10.48550/arXiv.1603.00423.

[11] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Jan. 30, 2017, arXiv: arXiv:1412.6980. doi: 10.48550/arXiv.1412.6980.

[12] F. Asferizal, “Analisis Perbandingan Kehandalan Data Hujan GSMaP, TRMM, GPM dan PERSIANN Terhadap Data Obsevasi Dalam Rentang Waktu Penelitian 2020 - 2021,” Journal of Infrastructure Planning and Design, vol. 2, no. 1, Art. no. 1, Jul. 2022.

[13] I. Permana and F. N. S. Salisah, “Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation: The Effect of Data Normalization on the Performance of the Classification Results of the Backpropagation Algorithm,” Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), vol. 2, no. 1, Art. no. 1, Mar. 2022, doi: 10.57152/ijirse.v2i1.311.

[14] J. Brownlee, Long Short-Term Memory Networks With Python: Develop Sequence Prediction Models with Deep Learning. Machine Learning Mastery, 2017.

[15] R. Tanjung, A. Listiani, and F. Lestari, “Prediksi Multivariate Time Series Parameter Cuaca Menggunakan Long Short - Term Memory (LSTM),” Prosiding Seminar Nasional Sains Data, vol. 4, no. 1, Art. no. 1, Sep. 2024, doi: 10.33005/senada.v4i1.253.

[16] R. F. Firdaus and I. V. Paputungan, “Prediksi Curah Hujan di Kota Bandung Menggunakan Metode Long Short Term Memory,” Jurnal Penelitian Inovatif, vol. 2, no. 3, pp. 453–460, Nov. 2022, doi: 10.54082/jupin.99.

[17] M. Q. Mahmood, X. Wang, F. Aziz, and T. Pang, “Evaluating the sustainability of groundwater abstraction in small watersheds using time series analysis,” Groundwater for Sustainable Development, vol. 26, p. 101288, Aug. 2024, doi: 10.1016/j.gsd.2024.101288.

[18] Y. Feng, Y. Zhang, and Y. Wang, “Out-of-sample volatility prediction: Rolling window, expanding window, or both?,” Journal of Forecasting, vol. 43, no. 3, pp. 567–582, 2024, doi: 10.1002/for.3046.

[19] T. Prasetyo, R. A. Putri, D. Ramadhani, Y. Angraini, and K. A. Notodiputro, “Perbandingan Kinerja Metode Arima, Multi-Layer Perceptron, dan Random Forest dalam Peramalan Harga Logam Mulia Berjangka yang Mengandung Pencilan,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 2, Art. no. 2, Aug. 2024, doi: 10.25126/jtiik.20241127392.

[20] CNN Indonesia, “Jadwal Lengkap Awal Musim Hujan di Bogor, Depok, Tangerang, dan Bekasi,” CNN Indonesia. Accessed: Mar. 10, 2025. [Online]. Available: https://www.cnnindonesia.com/teknologi/20241022172439-641-1158456/jadwal-lengkap-awal-musim-hujan-di-bogor-depok-tangerang-dan-bekasi

[21] S. Hidayatulah, “BPBD Prediksi Musim Kemarau di Bogor Hingga Oktober 2023 - Pakuan Raya.” Accessed: Mar. 10, 2025. [Online]. Available: https://pakuanraya.com/bpbd-prediksi-musim-kemarau-di-bogor-hingga-oktober-2023/

[22] A. ADRI, “Kabupaten Bogor Mulai Dilanda Kekeringan,” kompas.id. Accessed: Mar. 10, 2025. [Online]. Available: https://www.kompas.id/baca/metro/2023/06/11/kabupaten-bogor-mulai-dilanda-kekeringan

[23] D. Kasihairani, R. Virgianto, and S. Risnayah, “Dampak El Niño Southern Oscillation Dan Indian Ocean Dipole Mode Terhadap Variabilitas Curah Hujan Musiman Di Indonesia,” Jun. 2014.

[24] M. D. A. Carnegie and C. Chairani, “Perbandingan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Memprediksi Curah Hujan,” Jurnal Media Informatika Budidarma, vol. 7, no. 3, Art. no. 3, Jul. 2023, doi: 10.30865/mib.v7i3.6213.

[25] W. li and Z. Liu, “A method of SVM with Normalization in Intrusion Detection,” Procedia Environmental Sciences, vol. 11, pp. 256–262, Jan. 2011, doi: 10.1016/j.proenv.2011.12.040.

[26] F. Masri, D. Saepudin, and D. Adytia, “Peramalan Seri Waktu Permukaan Laut Menggunakan Deep Learning Rnn, Lstm, Dan Bilstm, Studi Kasus Di Teluk Jakarta, Indonesia,” eProceedings of Engineering, vol. 7, no. 2, Art. no. 2, Aug. 2020, Accessed: Mar. 10, 2025. [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/12760

[27] S. S. Chai and K. L. Goh, “Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network,” Journal of Optimization in Industrial Engineering, vol. 15, no. 1, pp. 187–193, Jun. 2022, doi: 10.22094/joie.2021.1941252.1899.

[28] S. Muzaffar and A. Afshari, “Short-Term Load Forecasts Using LSTM Networks,” Energy Procedia, vol. 158, pp. 2922–2927, Feb. 2019, doi: 10.1016/j.egypro.2019.01.952.

[29] N. A. Putri and A. Wibowo, “Rainfall Maps For The Suitability Of Settlement Area In Bogor Raya,” EnviroScienteae, vol. 19, no. 2, pp. 123–129, May 2023, doi: 10.20527/es.v19i2.15116.

[30] R. Wulandari, T. Rahmawati, A. Asyary, and F. Nugraha, “Analysis of Climate and Environmental Risk Factors on Dengue Hemorrhagic Fever Incidence in Bogor District,” Kesmas, vol. 18, no. 3, pp. 209–214, Aug. 2023, doi: 10.21109/kesmas.v18i3.7351.

[31] E. Khyber, L. Syaufina, and A. Sunkar, “Variability and time series trend analysis of rainfall and temperature in Dramaga Sub-District, Bogor, Indonesia,” IOP Conf. Ser.: Earth Environ. Sci., vol. 771, no. 1, p. 012016, May 2021, doi: 10.1088/1755-1315/771/1/012016.

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Published

2025-03-17

How to Cite

[1]
N. A. Fadhilah, “Long Short-Term Memory as a Rainfall Forecasting Model for Bogor City in 2025-2026”, JAIC, vol. 9, no. 2, pp. 333–340, Mar. 2025.

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