Related Experiment Video
Updated: Jul 14, 2026
08:17
Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation
Published on: August 14, 2020
5.0K
Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom
Jungsu Park1, Byeongchan Seong2, Yeonjeong Park3
1Department of Civil and Environmental Engineering, Hanbat National University, Republic of Korea.
Summary
Machine learning accurately predicts chlorophyll-a concentrations, a key indicator of algal blooms, using water quality data. Explainable AI identified essential variables, enabling cost-effective, real-time water quality management.
Area of Science:
- Environmental Science
- Data Science
- Water Resource Management
Background:
- Chlorophyll-a (Chl-a) concentration is a critical indicator for monitoring algal blooms.
- Accurate Chl-a estimation is vital for effective water quality management.
Purpose of the Study:
- To estimate Chl-a concentrations using the XGBoost machine learning model.
- To evaluate model performance across different data frequencies and identify key predictive variables.
- To enhance the practical applicability of machine learning models for on-site water quality management.
Main Methods:
- Employed the XGBoost machine learning model with 23 water quality and meteorological variables.
- Evaluated model performance using RMSE, RSR, and Nash-Sutcliffe efficiency across nine datasets with varying time frequencies (1 hour to 1 month).
- Utilized Shapley value (SHAP) analysis, an explainable AI method, to understand variable importance and impact.
Main Results:
- XGBoost model performance was stable for high-frequency data (1-24 hours) with RSR between 0.61-0.65.
- Model performance significantly declined with weekly and monthly data intervals.
- SHAP analysis revealed that stable model performance was achieved with five or fewer key variables, including pH, dissolved oxygen, and turbidity.
Conclusions:
- Machine learning models, particularly XGBoost, can effectively estimate Chl-a concentrations.
- Explainable AI (XAI) is crucial for understanding model behavior and identifying essential predictive variables.
- The study demonstrates the feasibility of using machine learning with real-time sensor data for practical water quality management.
