## 3 The Cyclic Evolutionary Network (CENS)
### Model Framework
The clustering results of the predicted South China Sea have been visualized. As indicated by the clustering results in Figure 8a and the status of continental shelf visualization in Figure 8b, the clustering boundary region of CENS is highly consistent with the boundary region of the continental shelf shown in Figure 8b in regions 1, 2, and 3. Such areas clustered by evolutionary networks are worthy of research in such ways of correlation analysis. #### 4.5.2 The Limitations of CENS

Parameter setting. CENS involves many hyperparameters, each of which can affect the prediction results. Therefore, the model necessitates experiments on parameter adjustment in practice. CENS is unsuited to small datasets. From the results in Figures 4 and 5, it can be seen that there is little room for improving the performance of CENS in predicting small-scale data, and more time is required. This is probably attributed to the fact that the features within the small-scale dataset are more consistent to the extent that they cannot be processed by CENS. The efficiency of the model is low. Compared with other deep learning models, the CENS model requires several cycles of training. As a result, the training time for CENS is several times longer compared to other models. Figure 8: Cluster result map of CENS and visualization of the submarine continental shelf. (**a**) is the clustering result of CENS. The color bar on the right side of (**a**) is the result of clustering. For example, red is the area of cluster 1, and orange is the area of cluster 2. (**b**) is the submarine continental shelf in the South China Sea. The depth of the color in the (**b**) indicates the water depth. ## 5 Conclusions

In this paper, a cyclic evolutionary network model (CENS) is proposed. As an error-driven network group, the model is comprised of multiple network node units, with the aim to facilitate training by splitting the dataset with different features. In order to achieve this goal, the prediction error of the model was treated as the division criterion of the dataset, and the dataset was divided automatically. Since different regions of data can be automatically matched to a suitable network node unit for prediction, the model can cluster the data based on their characteristics, thus making it more practical. Our method is designed to improve the accuracy of the model by automatically clustering the regions with different characteristics in the sea area. Through a comparison with different methods such as SVR, GBDT, GED, and RC-LSTM, it has been verified that CENS can achieve a higher accuracy in SST prediction. In general, this model is a relatively novel and effective solution. Conceptualization, J.S. and H.X. ; methodology, H.X. ; software, J.S. ; validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and L.X. ; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]) and [PERSON]; resources, [PERSON] ([PERSON]) and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing.J.Y. ([PERSON]); visualization, [PERSON]; supervision, [PERSON] ([PERSON]) and [PERSON]; project administration, [PERSON] ([PERSON]) and L.X. ; funding acquisition, [PERSON] ([PERSON]) and L.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Program on Key Research Project of China, grant number 2016 YFC1401900. Publicly available datasets were analyzed in this study. This data can be found here: [[https://psl.noaa.gov/](https://psl.noaa.gov/)]([https://psl.noaa.gov/](https://psl.noaa.gov/)). The authors declare no conflict of interest. ## References

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