Air quality prediction based on Long Short-Term Memory Model with advanced feature selection and hyperparameter optimization

Abstract

Good air quality is one of the prerequisites for stable urban economic growth and sustainable development. Air quality is influenced by a range of environmental elements. In this study, seven common air pollutants and six kinds of meteorological data in a major city in China are studied. In this urban setting, the air quality index will be estimated based on a Long Short-term Memory (LSTM)model. To improve prediction accuracy, the Random Forest (RF) method is adopted to choose important features and pass them to the LSTM model as input, an improved sparrow search algorithm (ISSA) is used to optimize the hyperparameters of the LSTM model. According to the experimental findings, the RF-ISSA-LSTM model demonstrates superior accuracy compared to both the basic LSTM model and the ISSA-LSTM fusion model.