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Air Quality Prediction in Smart City

Technological and industrial advancement is one of the main problems and causes of environmental pollution. Industry, power plants, traffic, factories are the main pollutants in big cities. On the other hand, the same technological advancement can also be used to decrease this bad influence. One of the ways is a smart city sensor network with applications that can predict air quality for the next few days and warn the population in the case of the upcoming severe pollution. The prediction task in this paper is conducted by using state-of-the-art algorithms such as CatBoost, with additional features that help improve the quality of the model. Three models are examined, regular CatBoost, CatBoost with additional variable, and the new approach where CatBoost model is boosted with another model. All the models are compared, while the best performance is achieved via the new approach Boosted CatBoost.