AN INTELLIGENT DEEP LEARNING BASED AQI PREDICTION MODEL WITH POOLED FEATURES

Abstract

Airborne pollution poses a significant threat to public health, leading to detrimental health effects. Despite global economicgrowth, ensuring access to clean air has become increasingly challenging worldwide. The contamination of air occurs as dust particlesand smoke, released by vehicles and industries, suspend into the atmosphere, exacerbating the challenge of providing clean air forpeople. Hence, it is imperative to predict the Air Quality Index (AQI) to safeguard the lives of people, especially considering the severe health effects caused by the inhalation of small particles. This paper outlines a deep learning methodology for constructing Air Quality Index (AQI) prediction models. The models utilize hourly meteorological data and pollutant information, aiming to fulfill the critical requirement for precise assessments of air quality. The aim of this paper is to formulate predictive models for AQI in Thiruvananthapuram, Kerala, employing deep learning algorithms, thereby addressing the escalating challenge of air pollution in theregion. Deep neural network architectures, such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM), and Gated Recurrent Unit (GRU), are implemented to construct the prediction model. When compared to other algorithms,GRU demonstrated promising outcomes. The findings of this research contribute not only to the advancement of AQI prediction models but also highlight the practical significance of employing deep learning techniques for accurate and timely air quality assessments. The outcomes have practical implications for public health and environmental management, providing a basis for informed decision-making in mitigating the adverse effects of air pollution.