Enhancing Rainfall Forecasting Performance in Bandung City Using Bi-LSTM with Grid Search Optimization on Gregorian and Lunar Calendar Data
Keywords:
Bi-LSTM, calendar conversion, rainfall forecasting, time series, tropical climatology
Abstract
Rainfall is a climatic factor that strongly influences human activities and plays a crucial role in decision making related to water resources, mobility, and disaster preparedness. High rainfall intensity may escalate into hydrometeorological hazards, underscoring the importance of accurate rainfall forecasting to support early warning and mitigation efforts. This study aims to compare the forecasting accuracy of monthly rainfall predictions between the Gregorian and lunar calendars using the Bidirectional Long Short-Term Memory (Bi-LSTM) model optimized through a grid search approach. The method is designed to capture temporal patterns arising from the distinct structures of two asynchronous calendars. Daily rainfall data from Bandung City, Indonesia, covering the period from 2000 to 2025, were converted into monthly series in both calendar systems. The results reveal that the Gregorian calendar provides significantly better forecasting performance, achieving the lowest MAPE value of 11.60 percent at the three-month horizon. In contrast, the lunar calendar shows higher variability and reaches its best MAPE of 31.43 percent at the same horizon. These findings indicate that the Gregorian calendar offers a more stable temporal representation for rainfall forecasting in Bandung and supports improved predictive modeling for climate-related decision making.
Downloads
Download data is not yet available.
References
[1] P. Feng et al., “Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia,” Environ. Res. Lett., vol. 15, no. 8, p. 84051, 2020.
[2] V. Masson-Delmotte et al., “Climate change 2021: the physical science basis,” Contrib. Work. Gr. I to sixth Assess. Rep. Intergov. panel Clim. Chang., vol. 2, no. 1, p. 2391, 2021.
[3] T. Kohyama and J. M. Wallace, “Rainfall variations induced by the lunar gravitational atmospheric tide and their implications for the relationship between tropical rainfall and humidity,” Geophys. Res. Lett., vol. 43, no. 2, pp. 918–923, 2016.
[4] S. Sen Roy, “Impact of lunar cycle on the precipitation in India,” Geophys. Res. Lett., vol. 33, no. 1, 2006.
[5] G. Darmawan, B. Handoko, D. Y. Faidah, and D. Islamiaty, “Improving the Forecasting Accuracy Based on the Lunar Calendar in Modeling Rainfall Levels Using the Bi‐LSTM Method Through the Grid Search Approach,” Sci. World J., vol. 2023, no. 1, p. 1863346, 2023.
[6] H. Kang, S. Yang, J. Huang, and J. Oh, “Time series prediction of wastewater flow rate by bidirectional LSTM deep learning,” Int. J. Control. Autom. Syst., vol. 18, no. 12, pp. 3023–3030, 2020.
[7] I. Priyadarshini and C. Cotton, “A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis,” J. Supercomput., vol. 77, no. 12, pp. 13911–13932, 2021.
[8] D. Endalie, G. Haile, and W. Taye, “Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia,” Water Supply, vol. 22, no. 3, pp. 3448–3461, 2022.
[9] Y. Zhang and A. Ye, “Machine learning for precipitation forecasts postprocessing: Multimodel comparison and experimental investigation,” J. Hydrometeorol., vol. 22, no. 11, pp. 3065–3085, 2021.
[10] A. Indryani, U. Khaira, and M. F. Putri, “Rainfall Prediction Using Long Short-Term Memory Method (Case Study: Jambi City),” Publ. Elektron. Pengemb. Apl. Digit. Untuk Negeri, vol. 6, no. 1, pp. 57–70, 2025, doi: https://doi.org/10.23960/pepadun.v6i1.256.
[11] R. Firdaus and I. Paputungan, “Prediksi Curah Hujan di Kota Bandung Menggunakan Metode Long Short Term Memory,” J. Penelit. Inov., vol. 2, pp. 453–460, Nov. 2022, doi: 10.54082/jupin.99.
[12] A. Wangwongchai, M. Waqas, P. Dechpichai, P. T. Hlaing, S. Ahmad, and U. W. Humphries, “Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques,” MethodsX, vol. 11, p. 102459, 2023.
[13] U. Rasool, X. Yin, Z. Xu, M. A. Rasool, M. Hussain, and F. Iftikhar, “Influence of lunar phases and meteorological factors on rainfall in Karachi City, Pakistan,” J. Hydrol., vol. 629, p. 130628, 2024.
[14] J. I. Avila-Carrazco et al., “Correlation between Lunar Phases and Rainfall Patterns in Mexico,” Atmosphere (Basel)., vol. 15, no. 7, p. 746, 2024.
[15] A. Ligthart, C. Catal, and B. Tekinerdogan, “Systematic reviews in sentiment analysis: a tertiary study,” Artif. Intell. Rev., vol. 54, no. 7, pp. 4997–5053, 2021.
[16] F. Rustam, M. Khalid, W. Aslam, V. Rupapara, A. Mehmood, and G. S. Choi, “A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis,” PLoS One, vol. 16, no. 2, p. e0245909, 2021.
[17] S. Mekruksavanich and A. Jitpattanakul, “Lstm networks using smartphone data for sensor-based human activity recognition in smart homes,” Sensors, vol. 21, no. 5, p. 1636, 2021.
[18] G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, “Sentiment analysis of comment texts based on BiLSTM,” Ieee Access, vol. 7, pp. 51522–51532, 2019.
[19] S. Liang, D. Wang, J. Wu, R. Wang, and R. Wang, “Method of Bidirectional LSTM Modelling for the Atmospheric Temperature.,” Intell. Autom. Soft Comput., vol. 30, no. 2, 2021.
[20] C. Cai, Y. Tao, T. Zhu, and Z. Deng, “Short-term load forecasting based on deep learning bidirectional lstm neural network,” Appl. Sci., vol. 11, no. 17, p. 8129, 2021.
[21] Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values,” Transp. Res. Part C Emerg. Technol., vol. 118, p. 102674, 2020.
[22] Z. Hameed and B. Garcia-Zapirain, “Sentiment classification using a single-layered BiLSTM model,” Ieee Access, vol. 8, pp. 73992–74001, 2020.
[23] J. Kim and N. Moon, “BiLSTM model based on multivariate time series data in multiple field for forecasting trading area,” J. Ambient Intell. Humaniz. Comput., pp. 1–10, 2019.
[24] P. Varalakshmi, N. Vasumathi, and R. Venkatesan, “Tropical Cyclone prediction based on multi-model fusion across Indian coastal region,” Prog. Oceanogr., vol. 193, p. 102557, 2021.
[25] F. Granata, F. Di Nunno, M. Najafzadeh, and I. Demir, “A stacked machine learning algorithm for multi-step ahead prediction of soil moisture,” Hydrology, vol. 10, no. 1, p. 1, 2022.
[26] R. K. Vogeti, B. R. Mishra, and K. S. Raju, “Machine learning algorithms for streamflow forecasting of Lower Godavari Basin,” H2Open J., vol. 5, no. 4, pp. 670–685, 2022.
[27] N. Sutarna, C. Tjahyadi, P. Oktivasari, M. Dwiyaniti, and T. Tohazen, “Hyperparameter Tuning Impact on Deep Learning Bi-LSTM for Photovoltaic Power Forecasting,” J Robot Control, vol. 4, no. 3, pp. 677–693, 2024, doi: https://doi.org/10.18196/jrc.v5i3.21120.
[28] A. G. AbdElkader, H. ZainEldin, and M. M. Saafan, “Optimizing wind power forecasting with RNN-LSTM models through grid search cross-validation,” Sustain. Comput. Informatics Syst., vol. 45, p. 101054, 2025, doi: https://doi.org/10.1016/j.suscom.2024.101054.
[29] M. A. Saleh, H. M. Rasel, and B. Ray, “A comprehensive review towards resilient rainfall forecasting models using artificial intelligence techniques,” Green Technol. Sustain., vol. 2, no. 3, p. 100104, 2024, doi: https://doi.org/10.1016/j.grets.2024.100104.
[30] L. S. Ee, M. Misiran, H. Sapiri, and Z. M. Yusof, “Forecasting the Monthly Temperature and Rainfall in Chuping Perlis,” Educ. J. Sci. Math. Technol., vol. 11, no. 2, pp. 20–33, 2024, doi: 10.37134/ejsmt.vol11.2.3.2024.
[31] S. Asiah, W. Riana, D. C. Purba, M. I. Azharsum, and P. V. A. Elyakim, “Annual Rainfall Prediction in Indonesia Using A Hybrid Artificial Neural Network and Fuzzy Algorithm Model,” JOMLAI J. Mach. Learn. Artif. Intell., vol. 4, no. 2, pp. 99–105, 2025, doi: https://doi.org/10.55123/jomlai.v4i2.5964.
[2] V. Masson-Delmotte et al., “Climate change 2021: the physical science basis,” Contrib. Work. Gr. I to sixth Assess. Rep. Intergov. panel Clim. Chang., vol. 2, no. 1, p. 2391, 2021.
[3] T. Kohyama and J. M. Wallace, “Rainfall variations induced by the lunar gravitational atmospheric tide and their implications for the relationship between tropical rainfall and humidity,” Geophys. Res. Lett., vol. 43, no. 2, pp. 918–923, 2016.
[4] S. Sen Roy, “Impact of lunar cycle on the precipitation in India,” Geophys. Res. Lett., vol. 33, no. 1, 2006.
[5] G. Darmawan, B. Handoko, D. Y. Faidah, and D. Islamiaty, “Improving the Forecasting Accuracy Based on the Lunar Calendar in Modeling Rainfall Levels Using the Bi‐LSTM Method Through the Grid Search Approach,” Sci. World J., vol. 2023, no. 1, p. 1863346, 2023.
[6] H. Kang, S. Yang, J. Huang, and J. Oh, “Time series prediction of wastewater flow rate by bidirectional LSTM deep learning,” Int. J. Control. Autom. Syst., vol. 18, no. 12, pp. 3023–3030, 2020.
[7] I. Priyadarshini and C. Cotton, “A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis,” J. Supercomput., vol. 77, no. 12, pp. 13911–13932, 2021.
[8] D. Endalie, G. Haile, and W. Taye, “Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia,” Water Supply, vol. 22, no. 3, pp. 3448–3461, 2022.
[9] Y. Zhang and A. Ye, “Machine learning for precipitation forecasts postprocessing: Multimodel comparison and experimental investigation,” J. Hydrometeorol., vol. 22, no. 11, pp. 3065–3085, 2021.
[10] A. Indryani, U. Khaira, and M. F. Putri, “Rainfall Prediction Using Long Short-Term Memory Method (Case Study: Jambi City),” Publ. Elektron. Pengemb. Apl. Digit. Untuk Negeri, vol. 6, no. 1, pp. 57–70, 2025, doi: https://doi.org/10.23960/pepadun.v6i1.256.
[11] R. Firdaus and I. Paputungan, “Prediksi Curah Hujan di Kota Bandung Menggunakan Metode Long Short Term Memory,” J. Penelit. Inov., vol. 2, pp. 453–460, Nov. 2022, doi: 10.54082/jupin.99.
[12] A. Wangwongchai, M. Waqas, P. Dechpichai, P. T. Hlaing, S. Ahmad, and U. W. Humphries, “Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques,” MethodsX, vol. 11, p. 102459, 2023.
[13] U. Rasool, X. Yin, Z. Xu, M. A. Rasool, M. Hussain, and F. Iftikhar, “Influence of lunar phases and meteorological factors on rainfall in Karachi City, Pakistan,” J. Hydrol., vol. 629, p. 130628, 2024.
[14] J. I. Avila-Carrazco et al., “Correlation between Lunar Phases and Rainfall Patterns in Mexico,” Atmosphere (Basel)., vol. 15, no. 7, p. 746, 2024.
[15] A. Ligthart, C. Catal, and B. Tekinerdogan, “Systematic reviews in sentiment analysis: a tertiary study,” Artif. Intell. Rev., vol. 54, no. 7, pp. 4997–5053, 2021.
[16] F. Rustam, M. Khalid, W. Aslam, V. Rupapara, A. Mehmood, and G. S. Choi, “A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis,” PLoS One, vol. 16, no. 2, p. e0245909, 2021.
[17] S. Mekruksavanich and A. Jitpattanakul, “Lstm networks using smartphone data for sensor-based human activity recognition in smart homes,” Sensors, vol. 21, no. 5, p. 1636, 2021.
[18] G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, “Sentiment analysis of comment texts based on BiLSTM,” Ieee Access, vol. 7, pp. 51522–51532, 2019.
[19] S. Liang, D. Wang, J. Wu, R. Wang, and R. Wang, “Method of Bidirectional LSTM Modelling for the Atmospheric Temperature.,” Intell. Autom. Soft Comput., vol. 30, no. 2, 2021.
[20] C. Cai, Y. Tao, T. Zhu, and Z. Deng, “Short-term load forecasting based on deep learning bidirectional lstm neural network,” Appl. Sci., vol. 11, no. 17, p. 8129, 2021.
[21] Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values,” Transp. Res. Part C Emerg. Technol., vol. 118, p. 102674, 2020.
[22] Z. Hameed and B. Garcia-Zapirain, “Sentiment classification using a single-layered BiLSTM model,” Ieee Access, vol. 8, pp. 73992–74001, 2020.
[23] J. Kim and N. Moon, “BiLSTM model based on multivariate time series data in multiple field for forecasting trading area,” J. Ambient Intell. Humaniz. Comput., pp. 1–10, 2019.
[24] P. Varalakshmi, N. Vasumathi, and R. Venkatesan, “Tropical Cyclone prediction based on multi-model fusion across Indian coastal region,” Prog. Oceanogr., vol. 193, p. 102557, 2021.
[25] F. Granata, F. Di Nunno, M. Najafzadeh, and I. Demir, “A stacked machine learning algorithm for multi-step ahead prediction of soil moisture,” Hydrology, vol. 10, no. 1, p. 1, 2022.
[26] R. K. Vogeti, B. R. Mishra, and K. S. Raju, “Machine learning algorithms for streamflow forecasting of Lower Godavari Basin,” H2Open J., vol. 5, no. 4, pp. 670–685, 2022.
[27] N. Sutarna, C. Tjahyadi, P. Oktivasari, M. Dwiyaniti, and T. Tohazen, “Hyperparameter Tuning Impact on Deep Learning Bi-LSTM for Photovoltaic Power Forecasting,” J Robot Control, vol. 4, no. 3, pp. 677–693, 2024, doi: https://doi.org/10.18196/jrc.v5i3.21120.
[28] A. G. AbdElkader, H. ZainEldin, and M. M. Saafan, “Optimizing wind power forecasting with RNN-LSTM models through grid search cross-validation,” Sustain. Comput. Informatics Syst., vol. 45, p. 101054, 2025, doi: https://doi.org/10.1016/j.suscom.2024.101054.
[29] M. A. Saleh, H. M. Rasel, and B. Ray, “A comprehensive review towards resilient rainfall forecasting models using artificial intelligence techniques,” Green Technol. Sustain., vol. 2, no. 3, p. 100104, 2024, doi: https://doi.org/10.1016/j.grets.2024.100104.
[30] L. S. Ee, M. Misiran, H. Sapiri, and Z. M. Yusof, “Forecasting the Monthly Temperature and Rainfall in Chuping Perlis,” Educ. J. Sci. Math. Technol., vol. 11, no. 2, pp. 20–33, 2024, doi: 10.37134/ejsmt.vol11.2.3.2024.
[31] S. Asiah, W. Riana, D. C. Purba, M. I. Azharsum, and P. V. A. Elyakim, “Annual Rainfall Prediction in Indonesia Using A Hybrid Artificial Neural Network and Fuzzy Algorithm Model,” JOMLAI J. Mach. Learn. Artif. Intell., vol. 4, no. 2, pp. 99–105, 2025, doi: https://doi.org/10.55123/jomlai.v4i2.5964.
Published
2026-01-05
Section
Articles
Copyright (c) 2026 Mahdayani Putri Yunizar, Andrew Hosea Talakua; Gumgum Darmawan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
