Optimization of CNN-LSTM Air Quality Prediction Based on the POA Algorithm

Jing Chang (), Jieshu Hou, He Gong and Yu Sun ()
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Jing Chang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jieshu Hou: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
He Gong: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yu Sun: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Sustainability, 2025, vol. 17, issue 12, 1-20

Abstract: Accurate prediction of Air Quality Index (AQI) is of significant importance for environmental governance. In this paper, the CNN-LSTM prediction model based on Pelican Optimization Algorithm (POA) was proposed to study the air quality index (AQI) and its influencing factors in Changchun. The model initially employs LightGBM and SHAP methods for feature engineering, constructs feature and label data, and increases the data dimensionality. The Pelican Optimization Algorithm (POA) is utilized to identify optimal performance parameters, ensuring the model achieves peak efficiency in parameter selection. The model evaluation showed that the mean absolute error was 4.2767, the root mean squared error is 6.7421, the coefficient of determination R-squared was 0.9871 and the explained variance score was 0.9877. The results of our study indicate the effectiveness of the POA-optimized CNN-LSTM prediction method in air quality forecasting. This model demonstrates the capacity to learn long-term dependencies and is well-suited for processing time series data.

Keywords: air quality; prediction model; optimization; LSTM; POA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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