Estimation of Air Quality Index from Seasonal Trends Using Deep Neural Network

Growing economy of a country is actually leading to harm for its atmosphere. Due to increase in the number of vehicles and industrial development in or around a city, air pollution has also escalated, which has started affecting health of the citizens. Therefore, the level of air pollution of a city needs to be monitored regularly in real-time to maintain the air quality. The state of the air of a city is described by a dimensionless value known as air quality index (AQI). In order to find a pattern from the time-series data, several techniques have been reported in literature such as linear regression, support vector machine, neural network. In this paper, we propose a method based on deep neural network architecture namely recurrent neural network (RNN) and memory cell called as long-short-term-memory (LSTM) for estimation of AQI of a city on future dates using the seasonal trends of the recorded time-series data. Simulation results confirm that the proposed method outperforms in terms of both root mean square error and Min/Max aggregation of AQI values compared to a state-of-the-art technique of AQI estimation.