# Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data
###### Abstract
investigated the phytoplankton community in the middle and lower reaches of the river from January to December 2012 and estimated the indices of species diversity and dominance. From January 2013 to July 2017 [10], conducted weekly examinations of the major environmental contributors to the water quality in eight of the river's weir pools and compared and analyzed their interrelationships with the dominant algal species [11]. used data on the chlorophyll a (Chl-a) content, algal species populations, water quality, and hydrological and meteorological factors in the Mulgeum area (a site located in the lower reaches of the Nakdong River) to correlate the changes in algal bloom patterns with changes in the influencing factors after construction of the veins [12]. conducted a comparative analysis of water quality factors at four points in the middle and lower reaches of the river after weir construction and used factor analysis to determine the correlation between those factors and blue-green algae populations [13]. Additionally, analyzed water quality data at the Mulgeum site, identified the fluctuations in algae density through the Chl-a water-quality factor, and suggested directions for improving the water quality through correlation analysis of the causes of algae outbreaks [14]. Other researchers have analyzed various features of algae using water quality models. For example, built a three-dimensional environmental fluid dynamics code (EFDC) model to evaluate the effects of changes in the hydrological characteristics (e.g., lower operational water level and higher flow rate) on the water quality and algal biomasses in the Chilgok and Gangjeong Goryeong weir sections of the Nakdong River [15]. To evaluate the reproducibility of the water temperature and dissolved oxygen (DO) stratification in the Nakdong River, built an EFDC model to cover the middle and lower reaches of the river and conducted a sensitivity analysis of the key parameters sensitive to stratification [16]. Algal blooms are difficult to predict and handle because they do not show constant regularity and are affected by a variety of environmental factors. Numerical models have been traditionally used to predict and respond to harmful algal blooms. However, such models are complex and require many computations, and sophisticated data-driven modeling is needed to obtain accurate results. With the recent advancements in data-driven technologies, such as machine learning, a hybrid data-driven/model-based approach is being used to predict the occurrence of blue-green algae. Because machine learning is used to determine patterns and relationships and create prediction models based on big data, it is a useful tool for predicting blue-green algae blooms. Compared with existing numerical models, data-driven/model-based approaches can reduce the reliance on numerical physics modeling and generate more accurate results by learning from the data. An early response to algae outbreaks is critical. The National Institute of Environmental Research is currently predicting algae outbreaks through numerical models, which enable highly accurate predictions based on the causal relationships between algae and water quality parameters [17]. Data-driven models help researchers identify particular patterns from large amounts of data, elucidate relationships between algal blooms and environmental factors, and understand the effects of these factors on algal growth. They are also easier to construct than numerical models [17; 18]. Several studies have predicted Chl-a concentrations, cyanobacterial cell densities, and algae alert levels by utilizing data-driven models to address the limitations of numerical models [19; 20; 21]. The decision tree algorithms used in this study--bagging, boosting, and random forest (RF)--are ensemble machine learning methods. The basic principle of an ensemble model is that a group of weak learners join to form a strong learner, thereby improving the accuracy of the model [22].