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The use of explainable artificial intelligence for interpreting the effect of flow phase and hysteresis on turbidity prediction

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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.

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Data Availability

The manuscript contains data representation itself, additional data will be made available on reasonable request.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1065518) (50%). This study [G21S302588802] was supported by the technology development project of the Ministry of SMEs in 2022 (50%).

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon, 34158, Republic of Korea

    Jungsu Park & Jin Chul Joo

  2. Division of Public Health Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA, 98109, USA

    Ilsuk Kang

  3. Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Dr., Orlando, FL, 32816, USA

    Woo Hyoung Lee

Authors
  1. Jungsu Park
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  2. Jin Chul Joo
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Contributions

JP: conceptualization, carried out the modeling and data analysis, investigation, methodology, writing—original draft, writing—review and editing. WHL: conceptualization, investigation, writing—review and editing. IK: conceptualization, investigation, writing—review and editing. JCJ: conceptualization, methodology, writing—review and editing.

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Correspondence to Ilsuk Kang or Woo Hyoung Lee.

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Park, J., Joo, J.C., Kang, I. et al. The use of explainable artificial intelligence for interpreting the effect of flow phase and hysteresis on turbidity prediction. Environ Earth Sci 82, 375 (2023). https://doi.org/10.1007/s12665-023-11056-1

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