Air Quality Improvement and Optimisation Using Machine Learning Technique

Abstract

Due to the increased use of automobiles, the manufacturing industry, and the emission of pollutants from other human activities, air pollution has risen above the expected safety level. Accurate estimating of the air quality index(AQI) is essential for effective pollution control. In this research, an AQI prediction ANFIS network model was created utilizing an already-existing data set. In this instance, the ANFIS system compares the performances of the back propagation neural network model, hybrid models, the Gaussian-BNN model, and the Gaussian-hybrid BNN model. Based on the actual raw data set, it was noted that the R and IA values of the Gaussian hybrid model are 0.9899. The ANFIS gauss-hybrid model might therefore be used to predict the most accurate model data.