Prediksi Temporal Medan Listrik Atmosfer Menggunakan Lstm Berbasis Data Electric Field Mill
DOI:
https://doi.org/10.55606/jtmei.v5i2.6236Keywords:
Atmospheric Electric Field, Electric Field Mill, Lightning Prediction, LSTM, Time SeriesAbstract
This study aimed to develop a temporal prediction model for atmospheric electric field variations using Long Short-Term Memory (LSTM) based on Electric Field Mill (EFM) data. Atmospheric electric field variation is an important indicator of charge accumulation in clouds and can support early warning systems for lightning potential. This study employed a descriptive quantitative approach using time-series data acquired from an ESP32-S3-based EFM system. The collected data included time, ADC value, raw voltage, filtered voltage, electric field value in kV/m, atmospheric status, and lightning potential. Before model training, the data were processed through low-pass filtering, Min-Max normalization, and sliding-window sequence formation with a window size of 60 observations. A Bidirectional LSTM architecture was applied for regression and classification tasks. The regression model produced an MSE of 0.558, RMSE of 0.747, and MAE of 0.580. Meanwhile, the classification model achieved an accuracy of 83.1% for atmospheric status classification and 99.20% for weather condition classification. These findings indicate that the proposed LSTM model can effectively learn temporal patterns in atmospheric electric field data and has potential to support the development of an early lightning warning system.
Downloads
References
Bao, R., Zhang, Y., Ma, B., Zhang, Z., & He, Z. (2022). An artificial neural network for lightning prediction based on atmospheric electric field observations. Remote Sensing, 14(17), 4131. https://doi.org/10.3390/rs14174131
Fukawa, M., Deng, X., Imai, S., Horiguchi, T., Ono, R., Rachi, I., A, S., Shinomura, K., Niwa, S., Kudo, T., Ito, H., Wakabayashi, H., Miyake, Y., & Hori, A. (2022). A novel method for lightning prediction by direct electric field measurements at the ground using recurrent neural network. IEICE Transactions on Information and Systems, E105.D(9), 1624–1628. https://doi.org/10.1587/transinf.2022EDL8026
Geng, Q., Wang, L., & Li, Q. (2024). Soil temperature prediction based on explainable artificial intelligence and LSTM. Frontiers in Environmental Science, 12, 1426942. https://doi.org/10.3389/fenvs.2024.1426942
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019). Deep learning for time series classification: A review. Data Mining and Knowledge Discovery, 33, 917–963. https://doi.org/10.1007/s10618-019-00619-1
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Leinonen, J., Hamann, U., & Germann, U. (2022). Seamless lightning nowcasting with recurrent-convolutional deep learning. Artificial Intelligence for the Earth Systems, 1(4), e220043. https://doi.org/10.1175/AIES-D-22-0043.1
Mostajabi, A., Finney, D. L., Rubinstein, M., & Rachidi, F. (2019). Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques. npj Climate and Atmospheric Science, 2, 41. https://doi.org/10.1038/s41612-019-0098-0
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Povschenko, O., & Bazhenov, V. (2023). Analysis of modern atmospheric electrostatic field measuring instruments and methods. Technology Audit and Production Reserves, 4(1[72]), 16–24. https://doi.org/10.15587/2706-5448.2023.285963
Rahimzad, M., Moghaddam Nia, A., Zolfonoon, H., Soltani, J., Danandeh Mehr, A., & Kwon, H.-H. (2021). Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting. Water Resources Management, 35, 4167–4187. https://doi.org/10.1007/s11269-021-02937-w
Sulaiman, M. H., Mohamed, A. I., & Mustaffa, Z. (2023). An application of deep learning for lightning prediction in East Coast Malaysia. E-Prime: Advances in Electrical Engineering, Electronics and Energy, 6, 100340. https://doi.org/10.1016/j.prime.2023.100340
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wang, X., Hu, K., Wu, Y., & Zhou, W. (2023). A survey of deep learning-based lightning prediction. Atmosphere, 14(11), 1698. https://doi.org/10.3390/atmos14111698
Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M. (2023). LSTM-autoencoder-based anomaly detection for indoor air quality time-series data. IEEE Sensors Journal, 23(4), 3787–3800. https://doi.org/10.1109/JSEN.2022.3230361
Yamashita, K., Fujisaka, H., Iwasaki, H., Kanno, K., & Hayakawa, M. (2022). A new electric field mill network to estimate temporal variation of simplified charge model in an isolated thundercloud. Sensors, 22(5), 1884. https://doi.org/10.3390/s22051884
Zhao, W., Li, Z., & Zhang, H. (2025). Research on the optimization of the electrode structure and signal processing method of the field mill type electric field sensor. Sensors, 25(13), 4186. https://doi.org/10.3390/s25134186
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muhammad Alfin Rafanda, Masayu Anisah, Ibnu Maja
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
