## 6 Conclusions and Discussion  
Previous studies using ANNs for benthic invertebrate communities have mainly been aimed at predicting populations and population densities in rivers. Various ecological factors (e.g., altitude, dissolved oxygen concentration, vegetation parameters, and water temperature) are usually used as input variables to allow for predicting the presence and abundance of benthic invertebrates [43]. In fact, many ecological factors can affect the aquatic beetle community structure, and the same ecological factors play different roles in different geographical regions. A predictive model based on ecological factors for one habitat may not be used to make predictions for a community in another habitat. Ecological factors for assessing or making predictions about insect communities should be selected after collecting samples and analyzing the data using reliable methods. The ecological factors used in this study were selected after assessing previously published research, so relatively ideal results were obtained. Machine learning has often been used to predict the populations of some insect species. Ecological factors affect each individual in a population in a stable and regular way, so these studies have achieved good results [13; 44]. A community is composed of many species, and each species has its own particular biological characteristics, so ecological factors affect each species differently. For example, changes in one factor can have different effects on the life histories, development stages, and development of different species. Moreover, the probability of being collected for each species is unequal even if the sampling method is the same. Therefore, it is acceptable that the predicted accuracy was lower than the accuracy of the population. We developed a nonlinear 1-D CNN prediction model based on field sampling data to study aquatic beetle communities in stagnant pools. The results indicated that it is feasible to use a 1-D CNN approach to assess or predict the grade of the number of individuals and the number of species in an aquatic beetle community. The reliability of the 1-D  
Figure 17: Number of species predicted by the 1-D CNN-based VAEGAN after excluding VRS and the actual number of species. Figure 18: Proportions of the actual number of species and the number of species predicted by the 1-D CNN-based VAEGAN after excluding VRS, with a difference of one to six species. CNN prediction model was affected by the number of samples. It is feasible to predict or assess a population when the accuracy is >80% [45; 46]. Our results were not ideal when 112 datasets were used to establish the 1-D CNN model. However, the accuracy for the number of species and the grade of the number of individuals were 85.0% and 86.0%, respectively, when sufficient data (>500 datasets) were used after expanding the data using the VAEGAN. We suggest that at least 500 datasets should be used to study insect communities using the 1-D CNN method. The actual value was the experimental data obtained in the field. If the population density of an aquatic beetle was very low and examples of the species were rarely collected, the number of aquatic beetles was grade 1. However, individuals of such a species can occasionally be collected during a long-term sampling campaign. The effect of such a species can be ignored when using the 1-D CNN model to predict community richness, although such a species will have an important impact on species diversity in the aquatic environment. When assessing an insect community using a neural network, the total number of all individuals is usually used as an output variable and only quantitative information about the community is assessed or predicted [1; 2]. The number of genera has also been used as an output variable when predicting community richness [36]. We used the 1-D CNN model to make aquatic beetle community predictions using both individual quantity information and species composition information as output variables.