Interpretation of ensemble learning to predict water quality using explainable artificial intelligence
Abstract
Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a commonly used indicator to obtain an estimation of algal concentration. In this study, an XGBoost ensemble machine learning (ML) model was developed from eighteen input variables to predict Chl-a concentration. The composition and pretreatment of input variables to the model are important factors for improving model performance. Explainable artificial intelligence (XAI) is an emerging area of ML modeling that provides a reasonable interpretation of model performance. The effect of input variable selection on model performance was estimated, where the priority of input variable selection was determined using three indices: Shapley value (SHAP), feature importance (FI), and variance inflation factor (VIF). SHAP analysis is an XAI algorithm designed to compute the relative importance of input variables with consistency, providing an interpretable analysis for model prediction. The XGB models simulated with independent variables selected using three indices were evaluated with root mean square error (RMSE), RMSE-observation standard deviation ratio, and Nash-Sutcliffe efficiency. This study shows that the model exhibited the most stable performance when the priority of input variables was determined by SHAP. This implies that on-site monitoring can be designed to collect the selected input variables from the SHAP analysis to reduce the cost of overall water quality analysis. The independent variables were further analyzed using SHAP summary plot, force plot, target plot, and partial dependency plot to provide understandable interpretation on the performance of the XGB model. While XAI is still in the early stages of development, this study successfully demonstrated a good example of XAI application to improve the interpretation of machine learning model performance in predicting water quality.
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An algal digital twin, as an integrated practice, helps to drive data-driven models and ecological sustainability initiatives using big data, IoT, AI and ML, and advanced computing for information gathering and analysis.
“An ADT, as an integrated practice, helps to drive data-driven (DD) models, and ecological sustainability initiatives [15,16] using BD, IoT, AI & ML, and advanced computing for informa tion gathering as well as analysis [17,18].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [16]
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- Authors
- Park, J., Lee, W. H., Kim, K. T., Park, C. Y., Lee, S., & Heo, T.-Y.
- Affiliations
- Hanbat National University (KR)
- University of Central Florida (US)
- Chungbuk National University (KR)
- Corresponding author
- Heo, Tae-Young
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 832
- Pages
- 155070
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W4223594669
- Cited by
- 243
- References
- 45
- Citations by year
- 2026: 39 · 2025: 83 · 2024: 75 · 2023: 38 · 2022: 8
- FWCI
- 18.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Science of The Total Environment (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Korea Environmental Industry and Technology Institute
- National Research Foundation of Korea
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Keywords
- algal bloom prediction, chlorophyll-a concentration, water quality prediction, XGBoost, ensemble learning, explainable AI, SHAP, feature importance, variance inflation factor, feature selection, freshwater ecosystems, partial dependence plots
- MeSH terms
- Machine Learning, Chlorophyll A, Algorithms, Artificial Intelligence, Water Quality
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