Survey Paper on Deep Learning Model based Air Pollution Forecasting Prediction
Keywords:
Air Quality Index (AQI), Machine Learning, Pollutant, Relative HumidityAbstract
Air pollution has become a critical environmental and health issue worldwide, necessitating accurate forecasting models to mitigate its impact. Traditional statistical and physical models have limitations in capturing complex spatiotemporal dependencies of air pollutants. Recently, deep learning models have demonstrated superior performance in air pollution forecasting by leveraging large datasets and capturing intricate patterns. This paper presents a comprehensive survey of various deep learning models applied to air pollution prediction, discussing their architectures, strengths, challenges, and potential future directions. So, the traditional computational intelligence models are not adequate to predict the weather accurately. Hence, deep learning-based techniques are employed to process massive datasets that can learn and make predictions more effectively based on past data. The effective implementation of deep learning in various domains has motivated its use in weather forecasting and is a significant development for the weather industry. The deep learning architectures like Recurrent Neural Networks, and Long Short-Term Memory Networks are proved to be reliable models for weather forecasting tasks.
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