Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review
Vibha Yadav, Amit Kumar Yadav, Vedant Singh, Tej Singh
Abstract
Air pollution in the environment is growing daily as a result of urbanization and population growth, which causes numerous health issues. Information about air quality and environmental health risks provided by air pollutant data is crucial for environmental management. The use of artificial neural network (ANN) approaches for predicting air pollutants is reviewed in this research. These methods are based on several forecast intervals, including hourly, daily, and monthly ones. This study shows that ANN techniques forecast air contaminants more precisely than traditional methods. It has been discovered that the input parameters and architecture-type algorithms used affect accuracy of air pollutant prediction models. ANN is therefore more accurate and reliable than other empirical models because they can handle a wide range of input meteorological parameters. Finally, research gap of neural networks for air pollutant prediction is identified. The review may inspire researchers and to a certain extent promote the development of artificial intelligence in air pollutant prediction.