Ozone Gases Value Forecasting Using Encoder-Decoder LSTM Model

Ni Ketut Intan Rahayu* (Bina Nusantara University, Jakarta, Indonesia)
Devi Fitrianah (Bina Nusantara University)
Elvin Elvin (Bina Nusantara University)
Tannuru Marthamurtadh (Bina Nusantara University)

Riwayat: Educational Journal of History and Humanities
Vol 6, No 3 (2023): Social, Political, and Economic History

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.