Cited by 243Aquatic Ecosystems and Phytoplankton DynamicsHydrological Forecasting Using AIWater Quality Monitoring and Analysis

Interpretation of ensemble learning to predict water quality using explainable artificial intelligence

Park, Jungsu, Lee, Woo Hyoung, Kim, Keug Tae, Park, Cheol Young, Lee, Sanghun, Heo, Tae-Young·2022·Science of The Total Environment

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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Crossref · 10.1016/j.scitotenv.2022.155070

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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].”

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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
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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