Utilizing deep learning models for accurate prediction of air pollution levels

Abstract

Air pollution has become a critical environmental issue, adversely affecting human health and the overall well-being of ecosystems. Accurate forecasting of air pollution levels is crucial for effective pollution management and mitigation strategies. In this study, we propose a deep learning-based model for air pollution forecasting that harnesses the power of neural networks to predict pollutant concentrations with high precision. Our approach involves training a deep learning model using historical air quality data, meteorological variables, and other relevant features. We leverage the temporal and spatial dependencies within the data to capture complex patterns and relationships. By incorporating information such as pollutant levels from previous time steps, meteorological conditions, and geographical factors, our model learns to effectively forecast air pollution levels. To train the deep learning model, we utilize a large dataset comprising historical air quality measurements from diverse monitoring stations. We preprocess the data, handle missing values, and normalize the features to ensure optimal training performance. The model architecture consists of multiple layers of interconnected neurons, enabling it to learn hierarchical representations of the input data.