## 4 Discussion  
### Similarities and Differences  
The SVM model has a better performance than the ENN model, which is consistent with the results of the previous study. According to the model evaluation results, the performance of the SVM simulation is higher than that of Elman. In a study for the time series prediction, it appeared that prediction results were equally well for the SVM model, whereas the Elman neural network could not be used to predict these series as well [35]. In a study in other areas, such as cyber security posture, the SVM model also showed more advanced predictions than the ENN model. In terms of error, the RMSE and MAE values of SVM were lower than ENN [36]. In these studies mentioned above, SVM showed better predictions than ENN, whether it was a time series prediction or a network safety period prediction.  
The runoff results predicted by the MMM model did not show an optimal prediction, which is inconsistent with the findings of most scholars, who generally agree that the MMM model has a significant advantage in balancing the internal variability of the data. As stated by [PERSON] et al. [37], MMM is a useful way of extracting first-order climate change projection information from the CMIP5 model. However, according to the scoring results of this study, the MMM model outperforms the ENN model in all metrics, while the overall scoring results are inferior to the SVM model, indicating that the prediction results of the MMM are not always better than those of a single model. Analysis of the results shows that the data of the MMM model are derived from both the SVM model and the MMM model, and although the MMM model can balance the data differences between the models, it does not completely reduce the runoff errors. As shown in Figure 6, the prediction error patterns of the SVM model and the ENN model are similar in the same period (both are overestimated or underestimated), thus the performance of the MMM model is in between the two models. In this study, it is reasonable for the prediction results of the MMM model to be between the best SVM model and the worst ENN model.  
The monthly data structure-based model did not outperform the sequential data structure-based model, which is different from other scholars' studies. According to [PERSON] et al. [32], the monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The analysis may be due to differences in model types leading to different results. In [PERSON]'s study, the IGWO-GRU model was applied to the runoff prediction, while the SVM model is applied in this paper. In addition, the runoff data in this study are based on karst basins, and the subsurface geological conditions are extremely different from those in non-karst areas. Karst has a continuous influence on runoff. Therefore, under the sequential structure, this may make the SVM model more effective in capturing the influence pattern of karst. Based on these two points, it is also reasonable that the sequential data structure-based model is superior to the monthly data structure-based model in runoff prediction.