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<p>Ozone concentration prediction is essential for managing air quality. Thus, our study develops the Stablized-long short-term memory (S-LSTM) model to predict one-day-ahead ozone concentration in the Khorasan province, Iran. The S-LSTM model has processing units that effectively control flow information. In addition, the S-LSTM model normalizes the outputs of its memory, allowing it to process large data sets. Our study uses meteorological parameters and pollutant variables to predict one-day-ahead ozone concentration. Results show that the S-LSTM model outperforms the standard LSTM model in forecasting ozone concentrations. The S-LSTM model improved the values of Kling–Gupta Efficiency (KGE), uncertainty at 95%, and Legates and McCabe Index (LMI) of the LSTM model by 9.4%, 73%, and 12%, respectively. The S-LTM model also improves the convergence speed of the LSTM model, which is important for modeling. Thus, the S-LSTM model can be utilized to monitor the concentrations of different atmospheric pollutants.</p>

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Stabilized Long Short Term Memory (SLSTM) model: a new variant of the LSTM model for predicting ozone concentration data

  • 期刊论文

  • 发表日期：

    2025年3月4日

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  • Fatemeh Kafi,
  • Elham Yousefi,
  • Mohammad Ehteram,
  • Khosro Ashrafi

摘要

Ozone concentration prediction is essential for managing air quality. Thus, our study develops the Stablized-long short-term memory (S-LSTM) model to predict one-day-ahead ozone concentration in the Khorasan province, Iran. The S-LSTM model has processing units that effectively control flow information. In addition, the S-LSTM model normalizes the outputs of its memory, allowing it to process large data sets. Our study uses meteorological parameters and pollutant variables to predict one-day-ahead ozone concentration. Results show that the S-LSTM model outperforms the standard LSTM model in forecasting ozone concentrations. The S-LSTM model improved the values of Kling–Gupta Efficiency (KGE), uncertainty at 95%, and Legates and McCabe Index (LMI) of the LSTM model by 9.4%, 73%, and 12%, respectively. The S-LTM model also improves the convergence speed of the LSTM model, which is important for modeling. Thus, the S-LSTM model can be utilized to monitor the concentrations of different atmospheric pollutants.