Abstract
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.
Highlights
Smart city design involves many aspects of the urban ecological environment, including a weather-smart grid, transportation, medical treatment, intelligent buildings, etc. [1]
Kong: Air Quality Predictive Modeling Based on an Improved Decision Tree in a Weather-Smart Grid various components in the air, the monitored air concentration was simplified to a single conceptual index
The time performance will become an important evaluation index of algorithms for air quality prediction, and the improved decision tree method in this paper provides a new idea for air quality prediction under a large amount of data, which will play an important role in protecting the natural environment and preventing air pollution
Summary
Smart city design involves many aspects of the urban ecological environment, including a weather-smart grid, transportation, medical treatment, intelligent buildings, etc. [1]. Kong: Air Quality Predictive Modeling Based on an Improved Decision Tree in a Weather-Smart Grid various components in the air, the monitored air concentration was simplified to a single conceptual index It classified the degree of air pollution and air quality status and was suitable for expressing the short-term air quality status and the changing trends in a city [8], [9]. With advancements in technology and research, many alternative methods have been proposed that use big-data and machine-learning approaches [10] These air quality models are limited by the computational costs. The improved algorithm has great advantages in accuracy and computational complexity It is more efficient in dealing with classification and prediction given a large amount of air quality data.
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