Ozone Gases Value Forecasting Using Encoder-Decoder LSTM Model

Ni Ketut Intan Rahayu*, Devi Fitrianah, Elvin Elvin, Tannuru Marthamurtadh


Climate change, as one of the impacts of global warming, has several consequences for the sustainability of living beings on Earth. It is necessary to monitor the trend of climate change. One way to monitor seasonal patterns of change is by analyzing the ozone content in the air. In addition to being an indicator of climate change, predicting the ozone gas content in the air is important because ozone gas has a direct impact on living organisms. By predicting the ozone gas content, it is hoped that preventive measures can be taken to prevent the adverse effects of ozone gas in the air. In the case of predicting ozone gases, there may be certain patterns that only become apparent over time, such as seasonal variations or long-term trends. A model that can capture these long-term dependencies will be better equipped to accurately predict ozone gas levels in the future. In this experiment, we proposed the use of Encoder-Decoder LSTM to predict ozone gas values.


Encoder-Decoder, Environmental Monitoring, Ozone, Long-Short-Term memory (LSTM)

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Abdul-Wahab, S. A., & Al-Alawi, S. M. (2002). Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling & Software, 17(3), 219–228.

Coman, A., Ionescu, A., & Candau, Y. (2008). Hourly ozone prediction for a 24-h horizon using neural networks. Environmental Modelling & Software, 23(12), 1407–1421.

Ettouney, R. S., Mjalli, F. S., Zaki, J. G., El‐Rifai, M. A., & Ettouney, H. M. (2009). Forecasting of ozone pollution using artificial neural networks. Management of Environmental Quality: An International Journal, 20(6), 668–683.

Jacoby, D., Ostrometzky, J., & Messer, H. (2021). Short-term prediction of the attenuation in a commercial microwave link using LSTM-based RNN. 2020 28th European Signal Processing Conference (EUSIPCO), 1628–1632.

Kök, İ., Şimşek, M. U., & Özdemir, S. (2017). A deep learning model for air quality prediction in smart cities. 2017 IEEE International Conference on Big Data (Big Data), 1983–1990.

Lyu, P., Chen, N., Mao, S., & Li, M. (2020). LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion. Process Safety and Environmental Protection, 137, 93–105.

Pires, J. C. M., Gonçalves, B., Azevedo, F. G., Carneiro, A. P., Rego, N., Assembleia, A. J. B., Lima, J. F. B., Silva, P. A., Alves, C., & Martins, F. G. (2012). Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting. Environmental Science and Pollution Research, 19, 3228–3234.

Sadhukhan, S., & Yadav, V. K. (2023). Forecasting, capturing and activation of carbon-dioxide (CO $ _2 $): Integration of Time Series Analysis, Machine Learning, and Material Design. ArXiv Preprint ArXiv:2307.14374.

Zoran, M. A., Savastru, R. S., Savastru, D. M., & Tautan, M. N. (2020). Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy. Science of The Total Environment, 740, 140005.

DOI: https://doi.org/10.24815/jr.v6i3.34435

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Riwayat: Educational of History and Humanities indexed by


Riwayat: Educational of History and Humanities

E-ISSN 2775-5037
P-ISSN 2614-3917

Published by History Education Department, Faculty of Teacher Training and Education, Universitas Syiah Kuala, Province Aceh. Indonesia
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