Experimental Evaluation of an Improved Air Quality Prediction Approach Based on AI Powered Deep Learning Mechanism

Everyone knows that pollution is becoming worse and worse, and that this is contributing to a sharp increase in global mortality rates; nevertheless, “Air-Pollution” is particularly harmful to humans. Air pollution is rapidly becoming a big threat to all forms of life on Earth, including humans. Officials in charge of public health are worried about the steadily declining air quality in our nation. Pollution levels are rising, which is causing the air quality index to rise daily. Air pollution not only adds to climate change, but it may also cause lung cancer, neurological diseases, and the greenhouse effect, all of which can be lethal. Reducing air pollution requires accurate air quality forecasts. Predicting the air quality index makes use of Machine Learning and Deep Learning techniques, despite the abundance of research on the subject. An improved learning for quality prediction strategy, or ILQPS, is developed in this research as a new deep learning model for detecting air quality contamination. In order to assess the efficacy of the suggested system, this model is cross-validated with the traditional learning model known as Linear Regression (LR). Supporting early warning and regional pollution control, the suggested solutions can greatly enhance public access to information on air quality forecast.