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Factors Influencing Transparency in Urban Landscape Water Bodies in Taiyuan City Based on Machine Learning Approaches

Author

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  • Yuan Zhou

    (College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, China)

  • Yongkang Lv

    (State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, Taiyuan 030024, China)

  • Jing Dong

    (College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, China)

  • Jin Yuan

    (College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, China
    Coshare Energy Environment, Taiyuan 030002, China)

  • Xiaomei Hui

    (College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, China)

Abstract

Urban landscape lakes (ULLs) in water-scarce cities face significant water quality challenges due to limited resources and intense human activity. This study identifies the main factors affecting transparency (SD) in these water bodies and proposes targeted management strategies. Machine learning techniques, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANNs), were applied to analyze SD drivers under various water supply conditions. Results show that, for surface water-supplied lakes, the GBDT model was most effective, identifying chlorophyll-a (Chl-a), inorganic suspended solids (ISS), and hydraulic retention time (HRT) as primary factors. For tap water-supplied lakes, ISS and dissolved oxygen (DO) were critical while, for rainwater retention bodies, the XGBoost model highlighted chemical oxygen demand (COD Mn ) and HRT as key factors. Further analysis with ANN models provided optimal learning rates and hidden layer configurations, enhancing SD predictions through contour mapping. The findings indicate that, under low suspended solid conditions, the interaction between HRT and ISS notably affects SD in surface water-supplied lakes. For tap water-supplied lakes, SD is predominantly influenced by ISS at low levels, while HRT gains significance as concentrations increase. In rainwater retention lakes, COD Mn emerges as the primary factor under low concentrations, with HRT interactions becoming prominent as COD Mn rises. This study offers a scientific foundation for effective strategies in ULL water quality management and aesthetic enhancement.

Suggested Citation

  • Yuan Zhou & Yongkang Lv & Jing Dong & Jin Yuan & Xiaomei Hui, 2025. "Factors Influencing Transparency in Urban Landscape Water Bodies in Taiyuan City Based on Machine Learning Approaches," Sustainability, MDPI, vol. 17(7), pages 1-31, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3126-:d:1625985
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    References listed on IDEAS

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    1. Sun, Shichao & Zhang, Hui & Zhou, Linlin & Wang, Ke, 2023. "Is the relationship between the perceived service quality and passenger loyalty linear or non-linear? A novel model-independent interpretation method is applied," Transport Policy, Elsevier, vol. 144(C), pages 65-79.
    2. Kristina Ek & Lars Persson, 2020. "Priorities and Preferences in Water Quality Management - a Case Study of the Alsterån River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 155-173, January.
    3. Yuan Zhou & Yongkang Lv & Jing Dong & Jin Yuan & Xiaomei Hui, 2024. "Sensitivity Analysis of Urban Landscape Lake Transparency Based on Machine Learning in Taiyuan City," Sustainability, MDPI, vol. 16(16), pages 1-18, August.
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