# [PERSON] Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
## 4 Conclusions  
The results in Section 3 demonstrate the versatility of the GANs for the synthetic data generation of weather radar images. Among selected variants of GANs, the WGAN-GP verifies its ability for the radar data augmentation qualitatively and quantitatively using the SSIM index. Additionally, the results showed that for generating the synthetic images under the same environment (Ubuntu 17.10, TITAN V, 16 GB RAM), the WGAN-GP takes less time to converge than other GANs as shown in Table 2. The WGAN-GP finishes its learning process within 8 min, while the InfoGAN consumes more than 17 h. The other GANs spend 7 to 12 h, which is also considerably longer than the WGAN-GP. Therefore, the WGAN-GP can be applied to make a database within a reasonable time by using a small amount of annotated examples by experts to solve challenging real-world problems, including qualitative precipitation estimation, convective storm prediction, and non-meteorological echo elimination. Future work needs to concentrate on applying the WGAN-GP to other features of weather radar, such as DR, KD, and RH, and combining them altogether as a color image. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GAN** & **CGAN** & **DCGAN** & **InfoGAN** & **LSGAN** & **WGAN-GP** \\ \hline \multirow{4}{*}{class 0} & & 0.1413 & 0.0002 & 0.0013 & 0.0007 & 0.0023 & **0.6245** \\  & & 0.1243 & 0.0003 & 0.0015 & 0.0006 & 0.0020 & **0.5687** \\  & & 0.1250 & 0.0002 & 0.0014 & 0.0006 & 0.0015 & **0.6022** \\  & & 0.1230 & 0.0002 & 0.0016 & 0.0007 & 0.0014 & **0.5455** \\  & & 0.1239 & 0.0002 & 0.0013 & 0.0007 & 0.0019 & **0.7570** \\ \cline{2-8}  & & 0.1349 & 0.0004 & 0.0025 & 0.0011 & 0.0025 & **0.5987** \\  & & 0.1174 & 0.0003 & 0.0021 & 0.0010 & 0.0018 & **0.5436** \\  & (Figure 2d) & 0.1178 & 0.0005 & 0.0020 & 0.0010 & 0.0013 & **0.5792** \\  & & 0.1161 & 0.0004 & 0.0021 & 0.0012 & 0.0013 & **0.5262** \\  & & 0.1169 & 0.0004 & 0.0022 & 0.0011 & 0.0016 & **0.7401** \\ \hline \multirow{4}{*}{class 1} & & 0.0058 & 0.0024 & 0.0073 & 0.0050 & 0.0068 & **0.5306** \\  & & 0.0091 & 0.0027 & 0.0078 & 0.0063 & 0.0073 & **0.4792** \\  & & 0.0097 & 0.0019 & 0.0064 & 0.0068 & 0.0069 & **0.4848** \\  & (Figure 2b) & 0.0136 & 0.0023 & 0.0073 & 0.0059 & 0.0086 & **0.4778** \\  & & 0.0034 & 0.0008 & 0.0061 & 0.0062 & 0.0073 & **0.4471** \\ \cline{2-8}  & & 0.0038 & 0.0016 & 0.0083 & 0.0061 & 0.0040 & **0.4424** \\  & & 0.0076 & 0.0024 & 0.0089 & 0.0061 & 0.0049 & **0.4803** \\  & (Figure 2e) & 0.0082 & 0.0012 & 0.0073 & 0.0065 & 0.0038 & **0.4379** \\  & & 0.0122 & 0.0016 & 0.0068 & 0.0062 & 0.0056 & **0.4258** \\  & & 0.0012 & 0.0027 & 0.0063 & 0.0058 & 0.0044 & **0.4054** \\ \hline \multirow{4}{*}{class 2} & & 0.0017 & 0.0151 & 0.0314 & 0.0468 & 0.0332 & **0.2584** \\  & & 0.0064 & 0.0116 & 0.0330 & 0.0453 & 0.0252 & **0.3878** \\  & (Figure 2c) & 0.0074 & 0.0077 & 0.0460 & 0.0450 & 0.0358 & **0.3425** \\  & (Figure 2c) & 0.0116 & 0.0086 & 0.0389 & 0.0429 & 0.0318 & **0.3322** \\  & & 0.0088 & 0.0095 & 0.0431 & 0.0426 & 0.0333 & **0.3127** \\ \cline{2-8}  & & 0.0387 & 0.0045 & 0.0876 & 0.1255 & 0.0899 & **0.3138** \\  & & 0.0130 & 0.0136 & 0.0975 & 0.1197 & 0.0876 & **0.3962** \\ \cline{2-8}  & (Figure 2f) & 0.0168 & 0.0158 & 0.1106 & 0.1164 & 0.0777 & **0.3422** \\  & & 0.0248 & 0.0048 & 0.1039 & 0.1215 & 0.0805 & **0.3118** \\  & & 0.0205 & 0.0194 & 0.1154 & 0.1129 & 0.0790 & **0.3106** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SSIM (structural similarity) based numerical comparison. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GAN** & **CGAN** & **DCGAN** & **InfoGAN** & **LSGAN** & **WGAN-GP** \\ \hline
**Time** & 27,930 s & 28,364 s & 42,950 s & 62,843 s & 42,893 s & 464 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Learning times for 300 epochs (seconds). **Author Contributions:** All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.