# Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data
## 4 Conclusions  
In this study, a category-type algae prediction model was developed to cope with algal blooms in the Nakdong River. As the complex mechanism of algae outbreaks is affected by various factors, we applied machine learning for the analysis. Additionally, real-time measurable factors were applied for analysis as a measure to handle algae outbreaks in advance. The eight weirs along the Nakdong River were clustered into four sections through cluster analysis. Then, a category-type prediction model was applied to the four sections. The machine learning algorithms applied in this study were RF, XGBoost, and CatBoost, of which RF showed the highest prediction performance. Additionally, the prediction performance was high when the model was based on only real-time measurable factors; that is, excluding factors related to water quality analysis. The results indicate that real-time prediction is possible through this approach. Moreover, it is also possible to respond to algae outbreaks in advance with the current algae alert system. The model can be used for predictions after 7 and after 14 days, allowing for algae response measures through river facility operation. The accuracy of sensor-based data may be relatively lower than that of water analysis data. Performance depends on the accuracy and stability of the sensor. Therefore, both sensor-based data and water analysis data should be applied together to improve the predictive power of the model beyond 14 days and to verify the predictions.  
Conceptualization, B.-G.J.; formal analysis, B.-G.J. and [PERSON]-[PERSON]; investigation, B.-G.J.; methodology, S.-H.N.; project administration, [PERSON]-G.J., [PERSON]-[PERSON] and [PERSON]-H.N.; resources, [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON]; validation, [PERSON]-G.J.; visualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--original draft, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through the Aquatic Ecosystem Conservation Research Program (or Project), funded by Korea Ministry of Environment (MOE) (2022003050007).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Day** & **Three Categories** & **Seven Categories** \\ \hline D & 0.831 & 0.782 \\ \hline D + 7 & 0.825 & 0.762 \\ \hline D + 14 & 0.803 & 0.744 \\ \hline D + 21 & 0.794 & 0.724 \\ \hline D + 28 & 0.757 & 0.683 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy analysis according to changes in category number.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[http://water.nier.go.kr/](http://water.nier.go.kr/)]([http://water.nier.go.kr/](http://water.nier.go.kr/)).  
**Acknowledgments:** The authors gratefully acknowledge the graduate students of Myongji University and Urban Research Office, Changwon Research Institute.  
**Conflicts of Interest:** The authors declare no conflict of interest.