PM2.5 concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China

Abstract

PM_2.5 is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM_2.5 concentrations is critical for assessing air pollution risks and providing early warnings for effective management. This study proposes a novel hybrid machine learning model that combines the whale optimization algorithm (WOA) with a convolutional neural network (CNN), long short-term memory (LSTM), and an attention mechanism (AM) to predict daily PM_2.5 concentrations. Tested with meteorological and air pollution daily data from 2014 to 2018, the WOA-CNN-LSTM-AM model demonstrates substantial improvements. It achieves MAE, RMSE, MBE, and R² values of 14.29, 21.96, −0.23, and 0.93, respectively, showing a reduction in prediction errors by 39% compared to CNN and 34% compared to LSTM models. In the medium-term forecast, the accuracy of the hybrid model is 30%–54% over WOA-CNN-LSTM and 26%–39% over CNN-LSTM-AM. The R² value decreases by 2.5% from the 1-day to 5-day forecast, maintaining high accuracy. SHAP analysis reveals that NO₂ and CO are the primary drivers for PM_2.5 predictions. This study provides a reliable tool for short and medium-term PM_2.5 prediction and air pollution control.