A NOVEL CONVOLUTIONAL NEURAL NETWORK FOR AIR POLLUTION FORECASTING

Abstract

Air pollution was a global problem a few decades back. It is still a problem and will continue to be a problem if not solved appropriately.Various machine learning and deep learining approaches have been purposed for accurate prediction, estimation and analysis of the air polution. We have purposed a novel five layer one-dimensional convolution neural network architecture to forecast the PM2.5 concentration. It is a deep learning approach. We have used the five year air pollution dataset from 2010 to 2014 recorded by the US embassy in Beijing, China taken from the database from UCI machine learining repository [19]. The dataset we are considering is in the .csv format. The dataset consists of feature columns like “Number,” “year,” “month,” “day,” “PM2.5”, “PM10”, “S02”, “dew,” “temp,” “pressure,” “wind direction,” “wind direction,” “snow” and “rain.” The dataset consisted of a total of 43,324 rows and nine feature columns.The model yields the best results in predicting PM2.5 levels with an RMSE of 28.1309 and MAE of 14.9727. On statistical analysis we found that ur proposed prediction model outperformed the traditional forecasting models like DTR, SVR and ANN models for the air pollution forecasting.