Air quality prediction model based on mRMR–RF feature selection and ISSA–LSTM

• Published in: Scientific reports Vol. 13; no. 1; p. 12825
• Main Authors: Wu, Huiyong, Yang, Tongtong, Li, Hongkun, Zhou, Ziwei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.08.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1314; Accuracy; Air pollution; Air quality; Algorithms; Humanities and Social Sciences; Long short-term memory; multidisciplinary; Outdoor air quality; Pollution control; Prediction models; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-39838-4

Severe air pollution poses a significant threat to public safety and human health. Predicting future air quality conditions is crucial for implementing pollution control measures and guiding residents' activity choices. However, traditional single-module machine learning models suffer from long training times and low prediction accuracy. To improve the accuracy of air quality forecasting, this paper proposes a ISSA–LSTM model-based approach for predicting the air quality index (AQI). The model consists of three main components: random forest (RF) and mRMR, improved sparrow search algorithm (ISSA), and long short-term memory network (LSTM). Firstly, RF–mRMR is used to select the influential variables affecting AQI, thereby enhancing the model's performance. Next, ISSA algorithm is employed to optimize the hyperparameters of LSTM, further improving the model’s performance. Finally, LSTM model is utilized to predict AQI concentrations. Through comparative experiments, it is demonstrated that the ISSA–LSTM model outperforms other models in terms of RMSE and R 2 , exhibiting higher prediction accuracy. The model's predictive performance is validated across different time steps, demonstrating minimal prediction errors. Therefore, the ISSA–LSTM model is a viable and effective approach for accurately predicting AQI.