# Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data
###### Abstract
Predictions based on the data of water analysis factors are accurate and reliable, because water collected from the field is measured using analytical equipment in the laboratory. However, the measurements needed to acquire the various water quality factors are time-consuming and costly, and real-time data are difficult to obtain. By contrast, sensors can be used to obtain data on the water quality of rivers in real time. Since real-time monitoring is possible, a quick response is also possible, and data can be collected at a relatively low cost compared with those obtained through water analysis. South Korea manages an algae alert system and reports the levels of these organisms on the basis of their cell density in freshwater [23]. However, current algae alert systems issue warnings based on collected water data only after the outbreak has occurred. To cope with algae outbreaks, methods for predicting when algae will bloom are needed; that is, proactive measures are as important as reactive ones. Therefore, the aim of this study was to apply machine learning to predict algae outbreaks on the basis of the data of various factors obtained through field measurements and sensors for real-time prediction. For this purpose, a decision tree algorithm (a type of machine learning) was developed to predict blue-green algae blooms in the Nakdong River. The decision tree algorithm facilitates easier understanding of the interactions between factors related to algal blooms according to the characteristics of the data and is expected to help identify the causes and mechanisms of algal bloom development. In this study, we attempted to predict the occurrence of blue-green algae targeting eight weirs in the Nakdong River. We attempted to predict the mass occurrence of blue-green algae early and utilize it for river management. A categorical forecast was performed based on the blue-green algae warning system. The factors used for prediction were based on water environment measurement network data provided by the Water Environment Information System. The model used was a decision tree-based algorithm. The performance evaluation for each algorithm and the prediction performance according to learning factors were compared. We attempted to compare prediction performance according to learning factors and evaluated the predictability of blue-green algae outbreaks through sensor-based factors. If blue-green algae exceeds a certain level, an alert is issued and the necessary measures are taken to ensure a stable supply of tap water. In addition, an algae warning system is being operated to ensure public safety from blue-green algae toxins during water-friendly activities. The current algae warning system collects and issues warnings after blue-green algae occurs. In order to respond to blue-green algae before it occurs in large quantities, it is necessary to predict the mass occurrence of algae. Reactive measures are necessary, but proactive measures are also needed. In addition, in order to respond to the occurrence of blue-green algae, the discharge amount changes depending on the weir operation plan according to the occurrence of blue-green algae. Currently, the Nakdong River is operating an automatic measuring station that measures water quality in real time. We sought to confirm the possibility of predicting algae for preemptive response by usingdata that can be collected in real time in areas where blue-green algae occur frequently. The automatic water quality measurement station installed by the Ministry of Environment was installed to measure and monitor water quality status and pollution levels in real time. This is operated as a device to monitor water pollution accidents in real time and respond quickly. Water quality data available from the national automatic water quality measurement network was used. In addition, real-time information management data provided by Mywater operated by K-water was used. Through this study, we sought to prepare countermeasures against large outbreaks of blue-green algae in rivers.