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With various developments, the concept of the smart city has attracted great attention all over the world. To many, it is a good intelligent response to the needs of people’s livelihoods, environmental protection, public safety, etc. A weather-smart grid is an important component of the smart city, and the health of the weather-smart grid will directly affect the health of the smart city. Efficient and accurate predictions about air quality levels can provide a reliable basis for societal decisions, safety for smart transportation, and weather-related disaster prevention and preparation. To improve the time performance and accuracy of prediction with a large amount of data, this paper proposes an improved decision tree method. Based on an existing method, the model is improved in two aspects: the feature attribute value and the weighting of the information gain. Both accuracy and computational complexity are improved. The experimental results demonstrate that the improved model has great advantages in terms of the accuracy and computational complexity compared with the traditional methods. Additionally, it is more efficient in addressing classification and prediction with a large amount of air quality data. Moreover, it has good prediction ability for future data.
Wang et al. (Tue,) studied this question.