## 3 Results
### Analysis Using Decision Tree  
In this study, a decision tree was constructed targeting three weir points located in the main stream of the Nakdong river to examine the conditions for the occurrence of blue-green algae, and their alarming levels were analyzed. The variables were analyzed by selecting those significant to the occurrence of blue-green algae through a variable-importance analysis of the RF. The decision-tree machine learning set comprised 80% of the total data, and the verification set comprised 20%. The target variable was set as a categorical criterion for the issuance of an algal bloom warning system; a classification tree was constructed; and optimization was performed through a pruning process based on the complexity parameter (CP) value to prevent overfitting of the model. Figures 3-5 graphically show the decision tree results. The purity of a node can be approximately checked by looking at its color. In addition, the ratio of each node to the total observation data is classified as the algal bloom warning system's grade. The first line in the box represents the ratings of the classified algal bloom warning system. The second row shows the percentages by grade. A level of blue-green algae cell count less than 1000 cells/mL is in the normal grade; a level of 1000-10,000 cells/mL is in the attention grade; and a level of 10,000 cells/mL or more is in the warning grade. The bottom row shows the percentage of observations included in the nodes of the total data. Figure 3: Results of the tree map of the conditions for cyanobacterial growth (ND). Among all the learning data of the ND area, the normal grade is 65%, the attention grade is 26%, and the warning grade is 9%. The occurrence conditions classified as node 4 of the tree map account for 55% of the total learning materials, and when the SPI_UP is \(-\)0.46 or more and the HRT is less than 14/day, the probability of becoming a normal grade is 87%. The occurrence conditions classified by node 15 account for 21% of the total learning materials, with an SPI_UP of \(-\)0.46 or less and \(-\)1.6 or more. When the temperature is below 21 \({}^{\circ}\)C, the probability of becoming an attention grade is 69%. In node 5, when the SPI_UP is \(-\)0.46 or higher and the HRT is 14/day, exceeding 2% of the total learning materials, the probability of becoming a warning grade is 100%. Among the total learning materials in the GG area, the normal grade comprises 46%, the attention grade is 36%, and the warning grade is 18%. The occurrence conditions classified as node 4 of the tree map are 50% of the total learning data, and the probability of being a normal grade is 70% when the DO is 8.4 mg/L or more and the pH is less than 8.8. The occurrence conditions classified as node 13 account for 25% of the total learning data, and when the DO is less than 8.4 mg/L, the EC is less than 272 \(\mu\)S/cm, and the temperature is above 20 \({}^{\circ}\)C, the probability of being an attention grade is 59%. At node 7, when 15% of the total learning materials are below a DO value of 8.4 mg/L and above an EC value of 272 \(\mu\)S/cm, the probability of being a warning grade is 72%. Figure 4: Results of the tree map of the conditions for cyanobacterial growth (GG). Figure 5: Results of the tree map of the conditions of cyanobacterial growth (HC). Among the total learning materials in the HC area, 46% are a normal grade, 25% are an attention grade, and 29% are a warning grade. The occurrence conditions classified as node 2 of the tree map account for 54% of the total learning materials. When the temperature is below 20 \({}^{\circ}\)C, the probability of being a normal grade is 68%. The occurrence conditions classified as node 26 are a temperature below 20 \({}^{\circ}\)C, an EC less than 347 uS/cm, an SPI_UP at \(-\)0.16 or more, and a BOD less than 2.4 mg/L; here, the probability of being an attention grade is 78%. In node No. 7, when the temperature is above 20 \({}^{\circ}\)C and the EC is above 347 uS/cm, the probability of becoming a warning grade is 96%.