## 5 Results
### Predictions Made by the 1-D CNN Model  
The results of the 1-D CNN (Figure 10) indicated that the relative errors for predicting the grade of the number of individuals ranged from 0.0 to 70.0%, and the average relative error was 26.0%. When the actual value was grade 1, the relative error ranged from 50.0 to 70.0%, and the average relative error of prediction was 62.5%. When the actual value was grade 2 or above, the relative error ranged from 0.0 to 50.0% and the average of relative error of prediction was 16.9%. The prediction accuracy was less than 80.0%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Type** & **Input** & **Convolution** & **FC** & **Output** \\ \hline \multirow{2}{*}{Kernel number} & 1-D CNN & - & 16 & - & - \\ \cline{2-6}  & 1-D CNN-based VAEGAN & & 24 & & \\ \hline \multirow{2}{*}{Kernel size} & 1-D CNN & - & [2,1] & - & - \\ \cline{2-6}  & 1-D CNN-based VAEGAN & & [4,1] & & \\ \hline \multirow{2}{*}{Stride} & 1-D CNN & - & 1 & - & - \\ \cline{2-6}  & 1-D CNN-based VAEGAN & & 1 & & \\ \hline \multirow{2}{*}{Padding} & 1-D CNN & - & 0 & - & - \\ \cline{2-6}  & 1-D CNN-based VAEGAN & & 0 & & \\ \hline \multirow{2}{*}{Activation} & 1-D CNN & - & LeakyReLU & - & - \\ \cline{2-6}  & 1-D CNN-based VAEGAN & & LeakyReLU & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Structural parameters of the CNN. Figure 9: The RMSE achieved by the model with the best performance. The number of species predicted by the 1-D CNN model is shown in Figure 11. The absolute differences between the predicted and actual values were calculated, and Figure 12 was drawn to show the proportion of the difference. It can be seen that when using the 1-D CNN to predict the number of species, 5.0% of the predicted values were the same as the actual values, 30.0% of the predicted values were one species different from the actual values, and 35.0% of the predicted values were two species different from the actual values. Field sampling inevitably leads to some errors, so we concluded that a difference of less than two species was a relatively reliable prediction. The predictions made by the 1-D CNN were therefore 70.0% reliable, but the accuracy of the prediction model was low. Figure 11: Numbers of species predicted by the 1-D CNN and the actual numbers of species. Figure 12: Proportions of the actual number of species and the number of species predicted by the 1-D CNN that were one to six species different. Figure 10: Grades of the numbers of individuals predicted by the 1-D CNN and measured in the field. ranged from 0.0 to 60.0%, and the average of relative error of prediction was 17.5%. When the actual value was grade 2 or above, the relative error ranged from 0.0 to 30.0%, and the average of relative error of prediction was 13.1%. On the whole, the average of relative errors of the 1-D CNN-based VAEGAN was much lower than that of the 1-D CNN before sample expansion. The number of species predicted by the 1-D CNN-based VAEGAN model is shown in Figure 14. The absolute differences between the predicted and actual values were calculated, and Figure 15 was drawn to show the proportion of the difference. It can be seen that when using the 1-D CNN-based VAEGAN to predict the number of species, 25.0% of the predicted values were the same as the actual values, 30.0% of the predicted values were two species different from the actual values. Field sampling inevitably leads to some errors, so we concluded that a difference of less than two species was a relatively reliable prediction. The predictions made by the 1-D CNN-based VAEGAN were therefore 85.0%. Compared with 1-D CNN before sample expansion, the accuracy of the number of species prediction of 1-D CNN-based VAEGAN significantly improved. A few very rare species (VRS) were sampled only once and only one individual was collected during one year of sampling. In order to reduce the impact of VRS on prediction in the community, a new model was established to exclude such data based on the 1-D CNN-based VAEGAN.