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

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.