Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks

The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.

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