Title: Air pollution prediction by using long-short-term memory neural network

Authors: Qinghua Xu; Jiankang Shen; Meng Gao

Addresses: School of Mathematics and Information Sciences, Yantai University, Yantai, 264005, China ' School of Mathematics and Information Sciences, Yantai University, Yantai, 264005, China ' School of Mathematics and Information Sciences, Yantai University, Yantai, 264005, China

Abstract: High ground-level ozone concentrations affect air quality, plant growth, and human health. This study uses an LSTM model to predict 1-h, 8-h, and 24-h ozone concentrations. We tested models with various hidden layer neurons and sequence lengths. Sensitivity to parameters rose with longer prediction intervals. After optimising hyperparameters, LSTM outperformed traditional methods like random forest and MLP in predicting ozone concentrations, with satisfactory predictive capability and pollution event warning rates. This validates the feasibility of LSTM models for predicting environmental ozone levels across different time intervals and confirms their effective ability to forecast air pollution incidents effectively.

Keywords: ozone; long-short-term memory; hyperparameters; time interval.

DOI: 10.1504/IJCSE.2025.148744

International Journal of Computational Science and Engineering, 2025 Vol.28 No.5, pp.585 - 594

Received: 04 Apr 2024
Accepted: 04 Jan 2025
Published online: 22 Sep 2025 *

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