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<p>Air pollution is one of the important problems of large cities today. This paper is devoted to the development of methods for predicting the concentration of fine particles PM2.5 in the atmosphere of Almaty. The main objective of the study is to evaluate the effectiveness of different neural architectures for predicting the concentration of PM2.5 in the air of Almaty. The study used data obtained from sensors installed at different points throughout the city. The paper focuses on recurrent neural networks and their modifications: LSTM (seq2vec), bidirectional LSTM (BiLSTM) and Seq2Seq for predicting the concentration of PM2.5. This allows us to compare the effectiveness of models depending on the window size. The results showed that LSTM is better at forecasting for 90&#xa0;days, Seq2Seq—for 180&#xa0;days, and BILSTM—for 365&#xa0;days. The application of these models can improve the air quality monitoring and management system in cities.</p>

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Predicting particulate matter (PM2.5) air pollution levels in Almaty city using machine learning techniques

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Alibek Issakhov,
  • Nurtugan Rysmambetov,
  • Aizhan Abylkassymova

摘要

Air pollution is one of the important problems of large cities today. This paper is devoted to the development of methods for predicting the concentration of fine particles PM2.5 in the atmosphere of Almaty. The main objective of the study is to evaluate the effectiveness of different neural architectures for predicting the concentration of PM2.5 in the air of Almaty. The study used data obtained from sensors installed at different points throughout the city. The paper focuses on recurrent neural networks and their modifications: LSTM (seq2vec), bidirectional LSTM (BiLSTM) and Seq2Seq for predicting the concentration of PM2.5. This allows us to compare the effectiveness of models depending on the window size. The results showed that LSTM is better at forecasting for 90 days, Seq2Seq—for 180 days, and BILSTM—for 365 days. The application of these models can improve the air quality monitoring and management system in cities.