A Prediction Model for Air Pollution using Artificial Neural Networks

Abstract

Abstract Over the past few decades, air pollution and preventive measures have proven scientifically challenging and the issue is still unending on a worldwide scale. The number of contaminants in the air is increasing daily as a result of the expanding population and the settling of more people in metropolitan regions. They have an impact on people's respiratory and cardiovascular systems, which raises the population's risk of disease and increases mortality. To better enhance public health, several attempts have been made by governmental organizations to comprehend and forecast the Air Quality Index. Without a doubt, the most crucial stage in prediction is the creation of a predictive model of the air quality, which will aid in environmental management and raise public awareness. The most important component of tracking air pollution is air quality prediction. Many methods will be useful in developing an effective model for pollution prediction. It is evident that the best approach for prediction is to use an artificial neural network. Therefore, this study was conducted by gathering data on air pollutants for the U.P. state city of MEERUT and creating an optimum model for the forecast of air quality.