# Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data
###### Abstract  
A multifunctional weir was built on the Nakdong River. As a result, changes in the river environment occurred, such as an increase in river residence time. This causes changes in water quality, including green algae. The occurrence of green algae in the Nakdong River, which is used as a water source, also affects the purified water supply system. In particular, the mass spread of harmful algae is becoming a major problem as the frequency and intensity of occurrences increase. There are various causes of blue-green algae. We would like to examine the relationships between causal factors through a decision tree-based algorithm. Additionally, we would like to predict the occurrence of green algae based on the combination of these factors. For prediction, we studied categorical prediction based on the blue-green algae warning system used in Korea. RF, Catboost and XGBoost algorithms were used. Optimal hyperparameters were applied. We compared the prediction performance of each algorithm. In addition, the predictability of using sensor-based data was reviewed for a preemptive response to the occurrence of blue-green algae. By applying sensor-based data, the accuracy was over 80%. Prediction accuracy by category was also over 75%. It is believed that real-time prediction is possible through sensor-based factors. The optimal forecast period was analyzed to determine whether a preemptive response was possible and the possibility of improvement was examined through the segmentation of prediction categories. When there were three categories, 79% of predictions were possible by the 21 st day. In seven categories, 75% prediction was possible up to 14 days. In this study, sensor-based categorical predictability was derived. In addition, real-time response and proactive response were determined. Such sensor-based algae prediction research is considered important for future blue-green algae management and river management. blue-green algae; sensor-based data; categorical prediction; decision trees; river management 20231322122661  
according to seasonal and temporal fluctuations and topographical changes in the region and water environment. In particular, sunlight and the temperature and stability of the water body and the nutrients contained therein contribute to algal blooms [3; 4]. As changes in the water temperature also affect the vertical movement of water bodies, a higher water temperature in early spring would cause algal seeds settled on the riverbed to rise to the water surface. In this way, algae introduced into the water body adapt to the water temperature and begin to compete for survival [5]. The surface water pH is a factor that strongly influences the growth of algal populations [6]. Blue-green algae prefer slightly alkaline environments (pH 7.7-9.4, which is the pH of natural waters) [7], and their growth is inhibited under slightly acidic conditions. The algal blooms in summer are derived from algal seeds that have been introduced into the water body in early spring. Ref. [5] emphasized the importance of understanding the mechanism of algal growth at the beginning of algal blooms and the advantage of focusing on this period for the implementation of algae control measures. Because blue-green algae blooms are caused by diverse factors, such as the quality and hydrological conditions of the water body as well as meteorological changes, the causal relationships between these factors and the patterns of algal blooms need to be clarified. Most studies on algal communities in the Nakdong River after completion of the Four Major Rivers Restoration Project focused on the downstream parts of the river, while few such studies have been carried out in the middle and upper reaches [8]. investigated the succession of blue-green algae in the mainstream section of the Nakdong River after weir installation and identified the morphological characteristics of the genus Dolichosperm that is commonly found in natural freshwater populations [9].