Читать книгу Cyberphysical Smart Cities Infrastructures - Группа авторов - Страница 31

Researchers in [5] leveraged DL algorithms and advanced communication technologies to link vehicles, roads, and drivers to facilitate and enhance various traffic‐related tasks and improve air pollution [6]. The scientists focused on different initiatives with the goal of creating a safe and smart environment and transportation. Further, Zhu et al. [37] developed an approach to use two algorithms: Bayesian inference and Random forest to execute in real time to predict the probability of crash occurrence to decrease crash risks in smart cities. Moreover, researchers [38] have established a combination of supervised and regression algorithms such as multivariate adaptive regression splines, classification and regression trees, and logistic regression to study the dataset of motor vehicle accident injury.

To have a safe environment, air pollution prediction in smart cities plays an imperative role. There have been a significant amount of work tried to enhance the prediction using several machine learning algorithms. The usage of machine learning algorithms to make environment safe has increased consistently, stating that how important this prediction would be for smart cities [6]. We examined the most relevant research studies [4, 6, 23, 38] but not limited to these, applying different evaluation based on several metrics. The evaluation of those research leads us to the following common observations: first, the rate of applying of the advanced (DL) machine learning algorithms has proliferated rather than typical machine learning algorithms; second, among the prediction elements for air pollution prediction, PM2.5 is considered as the most popular element; third, the data used for air pollution prediction had already generated hourly rather than daily; and in the final observation, efficient prediction occurs when air‐quality captured data merged with other relevant data of other networks like healthcare network within the smart cities.