Enhancing PM2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms

Abstract

Air pollution presents significant risks to both human health and the environment. This study uses air pollution and meteorological data to develop an effective deep learning model for hourly PM2.5 concentration predictions in Tehran, Iran. This study evaluates efficient metaheuristic algorithms for optimizing deep learning model hyperparameters to improve the accuracy of PM2.5 concentration predictions. The optimal feature set was selected using the Variance Inflation Factor (VIF) and the Boruta-XGBoost methods, which indicated the elimination of NO, NO2, and NOx. Boruta-XGBoost highlighted PM10 as the most important feature. Wavelet transform was then applied to extract 40 features to enhance prediction accuracy. Hyperparameters and weights matrices of the Echo State Network (ESN) model were determined using metaheuristic algorithms, with the Salp Swarm Algorithm (SSA) demonstrating superior performance. The evaluation of different criteria revealed that the ESN-SSA model outperformed other hybrids and the original ESN, LSTM, and GRU models.