A deep learning approach for prediction of air quality index in a metropolitan city

Abstract

In India, the Central and State Pollution Control Boards have commissioned the National Air Monitoring Program (NAMP) which covers 240 cities with 342 monitoring stations. Air Quality Index (AQI) has been categorized into different groups. To predict the AQI in Chennai city, the Dataset was collected, then preprocessed to replace missing values and remove redundant data. The mean, mean square error and standard deviation are extracted using the Grey Level Co-occurrence Matrix (GLCM). The combination of Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) based deep learning model is used to classify the AQI values. The proposed deep learning model gives an accurate and specific value for AQI on the city’s specified location compared to the existing techniques. The prediction accuracy is improved in the proposed deep learning method, which will caution the public to reduce to an acceptable level. The deep learning mechanism predicts the AQI values accurately and helps to plan the metropolitan city for sustainable development. The expected AQI value can control the pollution level by incorporating road traffic signal coordination, encouraging the people to use public transportation, and planting more trees on some locations.