BWO-BiLSTM & CNN composite model for prediction of atmospheric particulate matter mass concentration

Abstract

Accurate prediction of the mass concentration of atmospheric particulate matter is of great significance for the rational formulation of atmospheric environment management strategies and the study of the spatial and temporal evolution of atmospheric pollutants. In order to solve the problems of low prediction accuracy and low efficiency of traditional prediction models, aiming at the nonlinear and stochastic characteristics of atmospheric particulate matter mass concentration changes, a composite prediction model based on Random Forest (RF) feature selection, Beluga Whale Optimization (BWO) algorithm, Convolutional Neural Network (CNN) and Bidirectional Long and Short-Term Storage Memory Neural Network (BiLSTM) is proposed in this paper. In this composite prediction model, the input variables are adjusted and screened through RF algorithm to reduce the network complexity. The weights and thresholds of CNN-BiLSTM are optimized using BWO to improve the prediction accuracy of the model. The publicly mass concentration data and Aerodynamic Particle Size Spectrometer (APS) measurements are used to train and compare with the predicted data. The experimental results indicate that this composite model has better prediction performance and prediction accuracy compared with the traditional single and the combined model. The fitting coefficient (R2) of PM2.5 prediction using publicly data and APS data can reach 0.8842 and 0.9762, respectively. The R2 for the prediction of PM10 using publicly data and APS data can reach 0.8635 and 0.976, respectively. It indicates that the model proposed in this paper has better generalization and robustness.