## 1 Introduction
[PERSON] (2017) analyzed water quality data for the water prohibition point in the lower reaches of the Nakdong river, identified the process of increasing and decreasing algae through Chl-a water quality items, and presented directions for water quality improvement through a correlation analysis. Comprehensive research is being conducted to maintain the maximum number of areas where the ecological food chain can operate and provide physicochemical and ecological alternatives, such as an effective supply of dissolved oxygen to the lower part of the water body and the installation of artificial habitats [9]. A previous study analyzed the characteristics of algae using water quality models; [PERSON] (2019) built a 3-dimensional Environmental Fluid Dynamics Code (EFDC) model for the Chilgok Weir and Gangjeong Goryeong Weir sections located in the Nakdong river. The effects of the same hydraulic property changes on water quality and algal biomass were evaluated [10]. [PERSON] (2017) evaluated the reproducibility of stratification by applying the EFDC model to the Nakdong river [11]. An EFDC model was built downstream, and a sensitivity analysis was performed on the key parameters sensitive to stratification. In addition, as a countermeasure for algae reduction in the Nakdong River, there is a study that quantitatively analyzed the reduction effect by numerically simulating the pulsed flow [12]. Research trends related to green algae have been identified in the Nakdong river, and studies have been conducted on the succession of blue-green algae, including taxonomic studies and community structure analysis, as well as correlations with water quality factors and occurrence characteristics of blue-green algae. Recently, several studies have been conducted to improve algal forecasting by predicting the occurrence of algae in advance through the combination of meteorological and water quality measurement data, which are the primary factors affecting the occurrence of algae, and operational data of water structures, such as river dam discharge. Technological development through the introduction of artificial intelligence technology, which has shown good results in many applications, has been applied [13; 14]. In data mining, the importance of data and learning materials is high. To train an artificial intelligence model with high reliability and excellent performance, data that are exponentially proportional to the complexity of the model are required. For models used for classification problems, the amount of data and the homogeneity between the amounts of data belonging to each class should be considered. This is because, when the amounts of data between classes are imbalanced, learning is biased toward a specific class. When the amount of data belonging to each class is similar, the model achieves a better performance [15]. In algal research, machine-learning methods, which can be used to predict test data, are used to build models based on training data. 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 gather to form a strong learner to increase the accuracy of the model [16]. Recent studies have used neural networks, such as recurrent neural network (RNN) and long short-term memory (LSTM), as components of machine learning to improve the accuracy of green algae prediction [17; 18]. In line with the recent trend of water management research, big data on water management in the Nakdong river basin were collected and configured as the input data for data mining in this study. The collected big data can be used as basic water management data for policy establishment. By utilizing data mining technology, we intend to establish an advanced water management system for improving water quality in the Nakdong river basin. Therefore, in this study, we aimed to investigate the characteristics of algal blooms using a data-based prediction model based on machine learning.