## 3 The Proposed Algorithm
\\ \hline \hline PM\({}_{2.5}\) & PM\({}_{10}\) & SO\({}_{2}\) & NO\({}_{2}\) & O\({}_{3}\) & CO & AQI \\ \hline
77 & 139 & 13 & 93 & 3 & 2 & 103 \\
71 & 125 & 12 & 83 & 3 & 1.8 & 95 \\
42 & 78 & 7 & 66 & 2 & 1.4 & 64 \\
38 & 67 & 7 & 67 & 2 & 1.3 & 59 \\
33 & 65 & 6 & 65 & 2 & 1.1 & 58 \\
23 & 51 & 6 & 58 & 3 & 1 & 51 \\Metropolis introduced SA and proposed an importance sampling method--i.e., accepting new states with probability--called the Metropolis criterion [39]. This is the basic idea of SA algorithms. [PERSON] et al. first proposed the simulated annealing algorithms in 1983 [40; 41]. SA makes the optimal solution asymptotically convergent and is widely used to solve optimization problems. In recent years, with the rapid increase of information, there has been a huge amount of data (big data) which is larger than the traditional data. Under such a large amount of AQI data, how to find useful data from it has become an important issue. DT is based on the tree structure, presenting the data rules, enabling analysts to understand the implicit knowledge of the data and interpret it, which is widely used in various fields [30; 31; 32]. However, before establishing the decision tree model, it is necessary to set its relevant parameters, which will affect the result. Under different parameter combinations, if the parameter values are not adjusted properly, the classification result will be poor. Because parameters minimum case (\(M\)) and the pruning confidence factor (\(CF\)) of the DT will be different due to different problems, it is very time-consuming to manually adjust them. Therefore, this paper proposes an intelligent algorithm combining DT and SA, and studies an algorithm based on AQI decision rule establishment and parameter optimization. Then, based on the intelligent algorithm of parameter optimization and decision rules, the urban air quality is predicted and analyzed. This study combines the advantages of DT and SA. DT generates decision rules, SA \begin{table}
\begin{tabular}{c c c} \hline
**No.** & **AQI** & **Air Quality Level** \\ \hline
1 & 0–50 & Level 1: Excellent \\
2 & 51–100 & Level 2: Good \\
3 & 101–50 & Level 3: Light pollution \\
4 & 151–200 & Level 4: Medium pollution \\
5 & 201–250 & Level 5: Heavy pollution \\
6 & 251–300 & Level 6: Serious pollution \\ \hline \end{tabular}
\end{table} 
Table 4: Relationship between AQI and air quality level. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**PM\({}_{\text{2.5}}\)** & **PM\({}_{\text{10}}\)** & **SO\({}_{\text{2}}\)** & **NO\({}_{\text{2}}\)** & **O\({}_{\text{3}}\)** & **CO** & **Level** \\ \hline  
& 2 \\ \hline \end{tabular}
\end{table} 
Table 5: Partial data for air quality level. converges to the global optimum, and the parameters minimum case (\(M\)) and the pruning confidence factor (\(CF\)) of the DT determined by SA. The rules extracted in this paper can be used to analyze the collected information and then forecast a new AQI. Figure 1 shows a flow chart of the proposed algorithm; the AQI dataset is pre-processed as training and testing data, then initial values for the parameters are proposed; after that, the initial solution can be generated randomly. The proposed algorithm begins with four parameters, namely \(I_{gen},\ T_{0},\ T_{f},\) and \(\lambda\), where \(I_{gen}\) denotes the number of generations, \(T_{0}\) represents the initial temperature, \(T_{f}\) represents the final temperature that stops the proposed algorithm if the current temperature is lower than \(T_{f}\), and \(\lambda\) is the coefficient controlling the cooling rate,, respectively. The current temperature \(T\) is set to be the same as \(T_{0}\).