Application of the Attention-Based LSTM Method for Rainfall Prediction in East Java

Zainal Arifin; Abu Tholib; Rian Hidayat

doi:10.29040/ijcis.v5i4.224

Application of the Attention-Based LSTM Method for Rainfall Prediction in East Java

Zainal Arifin, Abu Tholib, Rian Hidayat

Abstract

This research aims to measure the performance of the Attention-Based Long Short-Term Memory (LSTM) predictive model in rainfall prediction analysis in East Java, with a focus on including the application of the model in predicting complex time-series data. The main objective of this study is to create an efficient and accurate model and to emit the performance of the Attention-Based LSTM algorithm compared to conventional methods. The methodology used includes rainfall data collection, data preprocessing, Attention-Based LSTM model design, training models, and testing to assess accuracy. The results of the study indicate that the Attention-Based LSTM model is able to improve rainfall prediction compared to conventional methods, with the Root Mean Squared Error (RMSE) evaluation metrics with a value of 0.00807 and Mean Squared Error (MSE) with a value of 0.08987 which shows better results, so this model can be relied on for real-world applications.

Full Text:

PDF

References

T.-T. Le, B. T. Pham, H.-B. Ly, A. Shirzadi, and L. M. Le, “Development of 48-hour precipitation forecasting model using nonlinear autoregressive neural network,” presented at the CIGOS 2019, innovation for sustainable infrastructure: Proceedings of the 5th international conference on geotechnics, civil engineering works and structures, Springer, 2020, pp. 1191–1196.

Zulkipli, “Produksi Padi Jawa Timur pada 2023 sekitar 9,59 juta ton gabah kering giling (GKG),” Oktober 2023. [Online]. Available: Https://Jatim.Bps.Go.Id/Pressrelease/2023/10/16/1385/Pada-2023--Luas-Panen- Padi.

M. A. R. Suleman and S. Shridevi, “Short-term weather forecasting using spatial feature attention based LSTM model,” IEEE Access, vol. 10, pp. 82456–82468, 2022.

W. Setiawan and A. Barokah, “Rainfall Prediction Using Backpropagation with Parameter Tuning,” presented at the MATEC Web of Conferences, EDP Sciences, 2022, p. 07003.

S. Sunardi, A. Yudhana, and G. Z. Muflih, “Sistem prediksi curah hujan bulanan menggunakan jaringan saraf tiruan backpropagation,” Jurnal Sistem Informasi Bisnis, vol. 10, no. 2, pp. 155–162, 2020.

G. U. Auliarahman, “Prediksi Curah Hujan Menggunakan Metode Regresi Bayesian di Kota Bandung pada Tahun 2022,” presented at the Bandung Conference Series: Statistics, 2023, pp. 265–272.

M. Jalgaonkar and D. Kulkarni, “Rainfall prediction using regression and multiple algorithms,” International Research Journal of Computer Science (IRJCS), vol. 8, no. 4, 2021.

C. Sekhar and P. S. Meghana, “A study on backpropagation in artificial neural networks,” Asia-Pacific Journal of Neural Networks and Its Applications, vol. 4, no. 1, pp. 21–28, 2020.

I. Budiman, A. Mubarak, S. Kapita, S. D. Abdullah, and M. Salmin, “Implementation of Backpropagation Artificial Network Methods for Early Children’s Intelligence Prediction,” presented at the E3S Web of Conferences, EDP Sciences, 2021, p. 04033.

V. Lestari, H. Mawengkang, and Z. Situmorang, “Artificial Neural Network Backpropagation Method to Predict Tuberculosis Cases,” Sinkron: jurnal dan penelitian teknik informatika, vol. 7, no. 1, pp. 35–47, 2023.

M. Wahyudi and L. Pujiastuti, “Application of Neural Network Variations for Determining the Best Architecture for Data Prediction,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 742–748, 2022.

M. Falah, D. P. Rini, and I. Pahendra, “Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi”.

Y. Shali, B. Brahma, R. Wadhvani, and M. Gyanchandani, “Attention LSTM for Time Series Forecasting of Financial Time Series Data,” presented at the International Conference on Internet of Things and Connected Technologies, Springer, 2020, pp. 74–84.

T. Lattifia, P. W. Buana, and N. K. D. Rusjayanthi, “Model Prediksi Cuaca Menggunakan Metode LSTM,” JITTER J. Ilm. Teknol. dan Komput, vol. 3, no. 1, pp. 994–1000, 2022.

M. Yusuf, A. Setyanto, and K. Aryasa, “Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression,” J-SAKTI (Jurnal Sains Komputer dan Informatika), vol. 6, no. 1, pp. 405–417, 2022.

F. Patriardian, A. Hidayati, R. Rahil Alzahira, D. Jhuandra Tasyant, and S. Anwar, “Peramalan curah hujan di Provinsi Aceh menggunakan metode Box-Jenkins (Rainfall forecasting in Aceh Province using Box-Jenkins methods),” Majalah Ilmiah Matematika dan Statistika, vol. 23, no. 1, 2022.

DOI: https://doi.org/10.29040/ijcis.v5i4.224