[논문]머신러닝 CatBoost 다중 분류 알고리즘을 이용한 조류 발생 예측 모형 성능 평가 연구

김준오; 박정수

doi:10.15681/kswe.2023.39.1.1

머신러닝 CatBoost 다중 분류 알고리즘을 이용한 조류 발생 예측 모형 성능 평가 연구
Evaluation of Multi-classification Model Performance for Algal Bloom Prediction Using CatBoost 원문보기

한국물환경학회지 = Journal of Korean Society on Water Environment, v.39 no.1, 2023년, pp.1 - 8

김준오 (국립한밭대학교 건설환경공학과) , 박정수 (국립한밭대학교 건설환경공학과)

Abstract ▼ AI-Helper

Monitoring and prediction of water quality are essential for effective river pollution prevention and water quality management. In this study, a multi-classification model was developed to predict chlorophyll-a (Chl-a) level in rivers. A model was developed using CatBoost, a novel ensemble machine learning algorithm. The model was developed using hourly field monitoring data collected from January 1 to December 31, 2015. For model development, chl-a was classified into class 1 (Chl-a≤10 ㎍/L), class 2 (10<Chl-a≤50 ㎍/L), and class 3 (Chl-a>50 ㎍/L), where the number of data used for the model training were 27,192, 11,031, and 511, respectively. The macro averages of precision, recall, and F1-score for the three classes were 0.58, 0.58, and 0.58, respectively, while the weighted averages were 0.89, 0.90, and 0.89, for precision, recall, and F1-score, respectively. The model showed relatively poor performance for class 3 where the number of observations was much smaller compared to the other two classes. The imbalance of data distribution among the three classes was resolved by using the synthetic minority over-sampling technique (SMOTE) algorithm, where the number of data used for model training was evenly distributed as 26,868 for each class. The model performance was improved with the macro averages of precision, rcall, and F1-score of the three classes as 0.58, 0.70, and 0.59, respectively, while the weighted averages were 0.88, 0.84, and 0.86 after SMOTE application.

주제어

표/그림 (10)

그림 Fig. 1. Research site.
표 Table 1. Hyperparameters used for the model optimization
그림 Fig. 2. Chl-a data used for model training and testing.
표 Table 2. Components of Confusion Matrix
표 Table 3. Characteristics of input variables according to classes
그림 Fig. 3. Performance comparison by model.
표 Table 4. Hyperparameters in optimized models
표 Table 5. CatBoost performance figures
그림 Fig. 4. Comparison of performance after SMOTE application.
표 Table 6. Hyperparameters in optimized models using SMOTE

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