Monitoring and Prediction of Air Quality using Neural Network

Abstract

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the neural network model has proposed.