Comparative Analysis of Daily and Weekly Heavy Rain Prediction Using LSTM and Cloud Data
DOI:
https://doi.org/10.26555/jiteki.v10i4.30374Keywords:
Weather, Heavy rain, Deep learning, LSTMAbstract
Indonesia's distinct geographic and climatic features make forecasting the weather there tricky. Due to its location at the equator and between two enormous oceans, the nation endures erratic weather patterns. Despite technical developments, the Meteorology, Climatology, and Geophysics Agency (BMKG) require assistance with precise forecasting. This research seeks to increase prediction accuracy using the Long Short-Term Memory (LSTM) algorithm, a deep learning technique appropriate for time series data processing. The research focuses on cloud data sets to improve the prediction of heavy rain. The potential of LSTM in weather forecasting has been demonstrated in earlier research, focusing on identifying rain at particular intervals. This research compares daily and weekly heavy rain prediction models using Python. Results reveal that the weekly model outperforms the daily model, achieving 85% accuracy compared to 80%. These findings highlight the effectiveness of LSTM in addressing the limitations of existing methods, offering a foundation for more reliable weather forecasting tailored to Indonesia’s conditions.
References
[1] Dwikorita, "BMKG Akui Prediksi Cuaca Tak 100 Persen Akurat" Berita Sains, CNN Indonesia, 22 Januari 2020. [Online]. Available: https://www.cnnindonesia.com/teknologi/20200121141153-199-467306/bmkg-akui-prediksi-cuaca-tak-100-persen-akurat.
[2] J. Byun, C. Jun, J. Kim, J. Cha and R. Narimani, "Deep Learning-Based Rainfall Prediction Using Cloud Image Analysis," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-11, no. 4701411, 2023, https://doi.org/10.1109/TGRS.2023.3263872 .
[3] M. F. Putranto and R. Munir, "Deep Learning Approach for Heavy Rainfall Prediction Using Himawari-8 And RDCA Data," International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 424-429, 2023, https:/doi.org/10.1109/IC3INA60834.2023.10285744.
[4] M. M. Hassan et al., "Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness," in IEEE Access, vol. 11, pp. 132196-132222, 2023, https://doi.org/10.1109/ACCESS.2023.3333876.
[5] N. Srinu and B. H. Bindu, "A Review on Machine Learning and Deep Learning based Rainfall Prediction Methods," International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), pp. 1-4, 2022, https://doi.org/10.1109/ICPECTS56089.2022.10047554.
[6] S. S. Ravi, R. G. K. Sai and J. R. R. R, "Design of Deep Learning Model for Predicting Rainfall," 8th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1343-1347, 2022, https://doi.org/10.1109/ICACCS54159.2022.9785028.
[7] V. K. Verma, H. S. Janagama and N. Patil, "An Efficient Rainfall Prediction Model Using Deep Learning Method," Third International Conference on Secure Cyber Computing and Communication (ICSCCC), pp. 566-572, 2023, https://doi.org/10.1109/ICSCCC58608.2023.10176598.
[8] Z. He, "Rain Prediction In Australia With Active Learning Algorithm," International Conference on Computers and Automation (CompAuto), pp. 14-18, 2021, https://doi.org/10.1109/CompAuto54408.2021.00010.
[9] Y. Gao and Y. Li, "Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention," 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA, pp. 1-6, 2022, https://doi.org/10.1109/CVIDLICCEA56201.2022.9824223.
[10] V. Monita,” Rainfall Prediction from Himawari-8 Data Using the Deep Learning Method,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), vol. 15, no. 2, pp. 47-59, 2024, https://doi.org/10.58346/JOWUA.2024.I2.004.
[11] R. Phadke, G. Mohamad Ramadan, R. Archana Reddy, A. Kumar Pani and H. M. Al-Jawahry, "Prediction of Rainfall Using Seasonal Auto Regressive Integrated Moving Average and Transductive Long Short-Term Model," International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), pp. 1-5, 2023, https://doi.org/10.1109/AIKIIE60097.2023.10390346.
[12] K. G, Y. Sushmitha, K. L. Saranya, P. Naga Ramya Sri and P. Amulya, "Rainfall Prediction Using Deep Learning and Machine Learning Techniques," International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pp. 1-7, 2023, https:/doi.org/10.1109/ACCAI58221.2023.10199905.
[13] K. Bessho et al., “An introduction to Himawari-8/9 — Japan’s new-generation geostationary meteorological satellites,” J. Meteorol. Soc. Japan. Ser. II, vol. 94, no. 2, pp. 151–183, 2016, https://doi.org/10.2151/jmsj.2016-009.
[14] F. Zhang, X. Wang, J. Guan, M. Wu, and L. Guo, “Rn-net: A deep learning approach to 0–2 h rainfall nowcasting based on radar and automatic weather station data,” Sensors, vol. 21, no. 6, pp. 1–14, 2021, https://doi.org/10.3390/s21061981.
[15] S. Kawasaki, Q. Wang, “Insolation Forecasting by using Image-processed Satellite Images Data and Forecasting of Cloud Imageries,” IEEJ Transactions on Power and Energy. vol.142, pp. 525-532, 2022, https://doi.org/10.1541/ieejpes.142.525.
[16] R. Lagerquist, J. Q. Stewart, I. Ebert-Uphoff, C. Kumler, “Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data,” Monthly Weather Review, vol 149, no. 12, pp. 3897–3921, 2021,
https://doi.org/10.1175/mwr-d-21-0096.1.
[17] Y. Sawada, K. Okamoto, M. Kunii, T. Miyoshi, “Assimilating every‐ 10‐minute Himawari‐8 infrared radiances to improve convective predictability,” Journal of Geophysical Research: Atmospheres, vol.124, pp. 2546–2561, 2019, https://doi.org/10.1029/2018JD029643.
[18] T. T. K, Tran, S. M. Bateni, S. J. Ki, H. Vosoughifar, “A Review of Neural Networks for Air Temperature Forecasting. Water (Swiss), vol. 13, no. 9, pp. 1–15, 2021, https://doi.org/10.3390/w13091294.
[19] W. Liu et al., “Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM,” Sensor, vol. 22, p. 5449, 2022, https://doi.org/doi.org/10.3390/s22145449.
[20] L. Song, G. Yu, J. Yuan, Z. Liu, ”Human pose estimation and its application to action recognition: A survey,” Journal of Visual Communication and Image Representation, vol 76, 103055, 2021, https://doi.org/10.1016/j.jvcir.2021.103055.
[21] T. Tashima, T. Kubota, T. Mega, T. Ushio and R. Oki, "Precipitation Extremes Monitoring Using the Near-Real-Time GSMaP Product," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5640-5651, 2020, https://doi.org/10.1109/JSTARS.2020.3014881.
[22] M. Momin, M. M. Alam, M. M. Hasan Mahfuz, M. R. Islam, M. Hadi Habaebi and K. Badron, "Prediction of Rain Attenuation on Earth-to-satellite Link using Rain Rate Measurement with Various Integration Times," 8th International Conference on Computer and Communication Engineering (ICCCE), pp. 385-390, 2021, https://doi.org/10.1109/ICCCE50029.2021.9467173.
[23] S. Ramanjaneyulu, S. E. Khaleelullla, R. K. Tenali, A. Rudraraju, S. Kaliappan and S. K. Pathuri, "Enhanced Rainfall Prediction: Leveraging Ensembling Models for Maximum Forecasting Performance," 3rd International Conference for Advancement in Technology (ICONAT), pp. 1-7, 2024, https://doi.org/10.1109/ICONAT61936.2024.10774805.
[24] G. Luo, L. Wang, Y. Zeng, Z. Chen, S. Wen and M. Zhang, "ESM-LSTM based optimisation model for weather prediction," 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 79-83, 2024, https://doi.org/10.1109/ICBASE63199.2024.10762687.
[25] K. Gupta, R. K. Gupta and A. Gupta, "Daily, Weekly and Monthly Rain Forecasting using Deep Learning," 3rd International Conference for Innovation in Technology (INOCON), pp. 1-6, 2024, https://doi.org/10.1109/INOCON60754.2024.10511700.
[26] S. Gupta, A. Moledina, S. Athavale, S. Gajare and M. Kate, "Air Quality Prediction Using Machine Learning: A Comparative Study," 6th International Conference on Advances in Science and Technology (ICAST), pp. 485-489, 2023, https://doi.org/10.1109/ICAST59062.2023.10454930.
[27] Maimunah, J. L. Buliali and A. Saikhu, "The Use of LSTM Model with Lagged Daily Inputs for Waste Disposal Prediction," Eighth International Conference on Informatics and Computing (ICIC), pp. 1-6, 2023, https://doi.org/10.1109/ICIC60109.2023.10382040.
[28] H. Rumapea, M. Sinambela, I. K. Jaya and I. M. Sarkis, "Prediction of Rainfall in North Sumatera Using Machine Learning," International Conference of Computer Science and Information Technology (ICOSNIKOM), pp. 1-4, 2023, https://doi.org/10.1109/ICoSNIKOM60230.2023.10364504.
[29] S. K. Gupta, P. Bhardwaj, R. Kumar, Pragya and A. Bag, "Rain-Prediction-system-Incorporated Automatic Water Feeding System for Agriculture Application," 9th International Conference on Smart Computing and Communications (ICSCC), pp. 173-177, 2023, https://doi.org/10.1109/ICSCC59169.2023.10335025.
[30] P. Paul, S. Basak and A. Khan, "A rainfall prediction system using Machine Learning (ML) and Internet of Things (IoT)," 8th International Conference on Computing in Engineering and Technology (ICCET 2023), Hybrid Conference, pp. 393-398, 2023, https://doi.org/10.1049/icp.2023.1522.
[31] R. Kumar and S. Arnon, "Experimental Modeling of Short-Term Effects of Rain on Satellite Link Using Machine Learning," in IEEE Transactions on Instrumentation and Measurement, vol. 72, no. 5503812, pp. 1-12, 2023, https://doi.org/10.1109/TIM.2023.3306825.
[32] P. P. G. Jaiswal et al., "A Stacking Ensemble Learning Model for Rainfall Prediction based on Indian Climate," 6th International Conference on Information Systems and Computer Networks (ISCON), pp. 1-6, 2023,https://doi.org/10.1109/ISCON57294.2023.10112077.
[33] C. Vijayalakshmi, K. Sangeeth, R. Josphineleela, R. Shalini, K. Sangeetha and D. Jenifer, "Rainfall Prediction using ARIMA and Linear Regression," International Conference on Computer, Power and Communications (ICCPC), pp. 366-370, 2022, https://doi.org/10.1109/ICCPC55978.2022.10072125.
[34] N. Srinu, S. Sivahari and M. R. Kale, "Leveraging Radial Basis Function Neural Networks for Rainfall Prediction in Andhra Pradesh," International Conference on Computer, Power and Communications (ICCPC), pp. 474-477, 2022, https://doi.org/10.1109/ICCPC55978.2022.10072177.
[35] L. Quibus, V. Le Mire, J. Queyrel, L. Castanet and L. Féral, "Rain Attenuation Estimation with the Numerical Weather Prediction Model WRF: Impact of Rain Drop Size Distribution for a Temperate Climate," 15th European Conference on Antennas and Propagation (EuCAP), pp. 1-5, 2021, https://doi.org/10.23919/EuCAP51087.2021.9410927.
[36] T. Tashima, T. Kubota, T. Mega, T. Ushio and R. Oki, "Precipitation Extremes Monitoring Using the Near-Real-Time GSMaP Product," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5640-5651, 2020, https://doi.org/10.1109/JSTARS.2020.3014881.
[37] X. Zhou, "Stock Price Prediction using Combined LSTM-CNN Model," 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 67-71, 2021, https://doi.org/10.1109/MLBDBI54094.2021.00020.
[38] S. Zhu, J. Yao and S. Z. Djokic, "Comparison of Two Modified Deterministic LSTM Models with a Probabilistic LSTM Model for a Day-Ahead Forecasting of Electricity Demands," IEEE Belgrade PowerTech, pp. 1-6, 2023, https://doi.org/10.1109/PowerTech55446.2023.10202847.
[39] Y. K. Joshi, U. Chawla and S. Shukla, "Rainfall Prediction Using Data Visualisation Techniques," 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 327-331, 2020, https://doi.org/10.1109/Confluence47617.2020.9057928.
[40] B. Setya, R. A. Nurhidayatullah, M. B. Hewen and K. Kusrini, "Comparative Analysis Of Rainfall Value Prediction In Semarang Using Linear And K-Nearest Neighbor Algorithms," 5th International Conference on Cybernetics and Intelligent System (ICORIS), pp. 1-5, 2023, https://doi.org/10.1109/ICORIS60118.2023.10352274.
[41] N. Tiwari and A. Singh, "A Novel Study of Rainfall in the Indian States and Predictive Analysis using Machine Learning Algorithms," International Conference on Computational Performance Evaluation (ComPE), pp. 199-204, 2020, https://doi.org/10.1109/ComPE49325.2020.9200091.
[42] P. V. S. Krithik, M. T. A. Raghavan, S. P. K and K. Vasanth, "Real Time Rainfall Prediction for Hyderabad Region using Machine learning Approach," Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1-6, 2021, https://doi.org/10.1109/i-PACT52855.2021.9697017.
[43] A. Raut, D. Theng and S. Khandelwal, "Random Forest Regressor Model for Rainfall Prediction," International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS), pp. 1-6, 2023, https://doi.org/10.1109/ICCAMS60113.2023.10526085.
[44] C. Z. Basha, N. Bhavana, P. Bhavya and S. V, "Rainfall Prediction using Machine Learning & Deep Learning Techniques," International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 92-97, 2020, https://doi.org/10.1109/ICESC48915.2020.9155896.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Vivi Monita, Sevirda Raniprima, Nanang Cahyadi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License
