콘텐츠 본문
논문 해외 국제일반학술지 The use of explainable artificial intelligence for interpreting the effect of flow phase and hysteresis on turbidity prediction
- 학술지 구분 국제일반학술지
- 게재년월 2023-08
- 저자명 Jungsu Park, Jin Chul Joo, Ilsuk Kang, Woo Hyoung Lee
- 학술지명 Environmental Earth Sciences
- 발행처명 Springer Berlin Heidelberg
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 4
- 논문 다운로드 링크(외부) https://link.springer.com/article/10.1007/s12665-023-11056-1
- 연구분야 공학 > 환경공학
논문 초록 (Abstract)
Predicting turbidity (T), which represents the amount of fine sediment in water, is essential in effective water quality management. In this study, two ensemble learning models, XGBoost and light gradient boosting decision tree (LGB), were employed to predict T, using discharge (Q) as an independent variable. The input variables were classified into three groups based on the flow phase: rising limb, falling limb, and base flow, where different time–frequency datasets (2, 8, and 24 h) were utilized to develop the model. In the first model set (Model 1), each model was trained separately for every phase, and their performance was tested by applying each to the corresponding Q. Another model set using XGBoost and LGB was developed by considering the entire period without classification for a comparison purpose (Model 2). The results demonstrated that Model 1 which used data classified into three phases outperformed Model 2. Further analysis of the flood phase and hysteresis in the relationship between Q and T showed that different data distributions in the three phases determined the performance differences between Models 1 and 2. By considering these differences, Model 1 exhibited better performance compared to Model 2. The Shapley additive explanation (SHAP), a novel explainable artificial intelligence method, provided a reasonable interpretation of the difference in model predictions between Models 1 and 2.
