## 4 Experimental Results
### The EMR-SF Construction Results by LRBF
#### 4.3.2 Accuracy Comparison  
To verify the accuracy of the LRBF spatial interpolation method, the LRBF spatial interpolation method is compared with the IDW and OK methods. In this paper, the root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), maximum error (\(MAXE\)), and minimum error (\(MINE\)) of the sampled data are calculated by the leave-one-out cross validation (LOOCV) method, which is used to evaluate the interpolation accuracy, and the calculated expressions are shown in Equations (7)-(10). \[RMSE=\sqrt{\frac{1}{m}\sum_{m=1}^{m}\left|f(x)^{\prime}-f(x)\right|^{2}} \tag{7}\]  
\[MAE=\frac{1}{m}\big{|}f(x)^{\prime}-f(x)\big{|} \tag{8}\]  
\[MAXE=\max\big{|}f(x)^{\prime}-f(x)\big{|} \tag{9}\]  
\[MINE=\min\big{|}f(x)^{\prime}-f(x)\big{|} \tag{10}\]  
where \(m\) is the number of sampled data, \(f(x)^{\prime}\) is the EMR value of the sampled data calculated using the LRBF method, and \(f(x)\) is the real EMR value of the sampled data. In the interpolation process, IDW and OK select 12 known points closest to the unknown point to calculate the EMR of the unknown point. The distance power of the IDW interpolation method is 2. The OK method uses a Gaussian function semi-variance model for fitting. The error statistics of each interpolation method are shown (Table 1). The accuracy of IDW and OK are comparable. The IDW uses the distance between unknown points and sample points as the weighted average, which is more suitable for use in the case of a uniform distribution of sampled data. The OK mainly relies on the spatial autocorrelation of the data, which requires a sufficient number of sample points with strong spatial autocorrelation to produce a better interpolation accuracy. The main reason for the comparable interpolation accuracy of the IDW and OK is that the data collected along the road are linearly distributed, and the contribution of the sample points obtained by the two methods within a certain search field is not much different. This leads to the weight proportion coefficients of the two not being much different, which results in similar interpolation results for both. In general, LRBF has a higher accuracy than the other two methods. The LRBF improves the approximation ability of RBF by increasing the sample points of RBF interpolation. At the same time, sample points are only added within a certain distance to ensure the shape characteristics of the RBF basis function. This improves the stability of the constructed surface function and thus improves the interpolation accuracy of LEBF. The results of the EMR-SF constructed by different interpolation methods are shown (Figure 16). IDW and OK construct the EMR-SF using sample points within a certain search range. However, the EMR data collected along the road have a large range of blank areas overall, and they are linearly distributed locally. It is easy to conclude that \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interpolation Method** & **Ordinary Kriging** & **IDW** & **LRBF** \\ \hline RMSE & 0.0582 & 0.0482 & 0.0378 \\ \hline MAE & 0.0295 & 0.0323 & 0.0179 \\ \hline MAXE & 0.50 & 0.47 & 0.45 \\ \hline MINE & \(0.8\times 10^{-5}\) & \(0.2\times 10^{-4}\) & \(0.3\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error of each interpolation method (\(v/m\)). the EMR intensity generated by IDW and OK in a certain range is the same. For example, IDW and OK only refer to the sample points on the main road for the EMR value on the north side of Teaching Building 1, so the EMR value they generate is in the same range as the value on the main road. However, the EMR value increases and then decreases within a certain distance from the transmitting antenna, and there is an obvious zoning phenomenon (Figure 11). Thus, the accuracy of constructing the EMR-SF of IDW and OK is low.