Machine Learning for Air Quality Prediction

  • Shreedhar Maruti Kumbhar Department of Masters of Computer Applications Rajarajeswari College of Engineering
  • Meghana R Department of Masters of Computer Applications Rajarajeswari College of Engineering
Keywords: Air Quality, Machine Learning, Electricty Prediction, Quillbot


Environmental protection measures cannot be properly guaranteed in the present as a result of rapid industrialization. The main issue hurting the the standard of living in country is the escalating severity of environmental issues. Therefore, in in order to comprehend the potential air pollution process beforehand, we need to put together a reasonably good air quality forecasting model. Setting up and implementing appropriate control measures to lessen Air pollution is extremely important, in accordance with model’s forecast results.Techniques for mining data, such as neural networks, mutual information theory, and intelligent optimisation algorithms are all extensively used in this article. As a training set and test set, we employ the fundamental information from open monitoring locations that predicts long-term air quality.Today, determining the the state of the air has emerged as among the most crucial significant activities for the residents of many industrial and metropolitan areas. Many kinds of pollution are caused by the usage of fuel, electricity, transportation, etc. have a detrimental effect on the air’s quality. The grade of living in smart cities is significantly being impacted by the buildup of hazardous gases. In accordance with address the escalating levels of air pollution, we must put into place efficient air quality testing and prediction models that collect information on pollutant concentrations and offer evaluations of regional air pollution. Inhaling these minuscule solid or liquid droplets, which make up particulate matter, can have a serious negative impact on one’s health.

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