## 4 Experiments and Results
### Results and Analysis
#### 4.2.2 Determination of the Number of Diverted Flights
When the restricted areas are opened and run according to the system settings, the aircraft in the system that is scheduled to fly over the restricted areas will be intercepted, and the intercepted aircraft will be those that need to be diverted due to bad weather. After using the data to train the simulation system, the number of diverted flights on different routes in different bad-weather grades could be obtained. The specific results are listed in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Route Path Number** & **Path** & **Proportion of Route Traffic** \\ \hline R1 & Exit in the direction of OPULI & 2.70\% \\ R2 & Exit in the direction of DOXED & 13.11\% \\ R3 & Exit in the direction of DOMVA & 24.80\% \\ R4 & Exit in BELIP direction & 8.75\% \\ R5 & Go in the direction of OPULI & 2.80\% \\ R6 & Advance in the direction of AGVEN & 13.16\% \\ R7 & Heading in the direction of IDGOB & 26.00\% \\ R8 & LUVKO heading in & 8.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of the proportion of route traffic in each route. Figure 9: Schematic diagram of flight-restricted areas of the approach and departure routes. square error value in each case is obtained according to the calculation. When the number of hidden layers is 2 and the number of nodes is 3 and 4, respectively, the mean square error of the system is the smallest 0.2267. Therefore, the model structure of the DNN algorithm is 2-[3-4]-1. According to the comparison with the expert evaluation criteria, the results of both methods are as follows in Table 8.  
It can be seen that the accuracy of the two methods is similar in this type of application. Considering that in the follow-up experiments, the number of variables will increase. The DNN algorithm can provide a good research model for studying the nonlinear relationship between multiple variables, and it's error correction of it can be used to obtain a more accurate result. Therefore, we choose to use the DNN algorithm in this paper. Ten groups of 1200 pieces of data with input variables were trained using the DNN forward-propagation algorithm. The input variables of each group were divided into sector flow, talk time of controllers per hour, talk times, diverted trips, and optimized weather-influence coefficients. There were 120 pieces of input sample data in each group. The workload of output variable controllers was calculated, and the output sample data are presented in Table 9.  
The model calculations have revealed the workload of controllers in the sector of Yinchuan Hedong International Airport under the influence of bad weather on 24 June 2019. Regression analysis was carried out between controller workload and the number of aircraft during this period, and the functional relationship between them was determined. The corresponding sector capacity during this period was calculated by the workload of controllers obtained in the model training. The calculation results are presented in Table 10. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & **Sector** & \multirow{2}{*}{**Time Interval**} & **Sector** \\  & **Capacity/Route** & & **Capacity/Route** \\ \hline
00:00–01:00 & 12 & 12:00–13:00 & 22 \\
01:00–02:00 & 16 & 13:00–14:00 & 23 \\
02:00–03:00 & 20 & 14:00–15:00 & 20 \\
03:00–04:00 & 23 & 15:00–16:00 & 20 \\
04:00–05:00 & 26 & 16:00–17:00 & 14 \\
05:00–06:00 & 27 & 17:00–18:00 & 22 \\
06:00–07:00 & 27 & 18:00–19:00 & 24 \\
07:00–08:00 & 26 & 19:00–20:00 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Sector capacities predicted by the model. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **Validation Results** \\ \hline DNN algorithm & 0.92529 \\ Hybrid Kernel SVM algorithm & 0.91835 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Validation results obtained by two methods.