Sensitivity Analysis of Urban Landscape Lake Transparency Based on Machine Learning in Taiyuan City

Yuan Zhou, Yongkang Lv, Jing Dong, Jin Yuan () and Xiaomei Hui
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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
Xiaomei Hui: College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, China

Sustainability, 2024, vol. 16, issue 16, 1-18

Abstract: This article addresses the challenge of maintaining water quality in urban landscape lakes in water-scarce cities using transparency as the key indicator. The sensitivity of water transparency to nine water quality parameters, including chlorophyll a and inorganic suspended solids, in 16 urban landscape lakes of the city of Taiyuan was evaluated using the Sobol and Morris sensitivity analysis methods. The results indicate that for water bodies supplied by surface water, critical factors include chlorophyll a and hydraulic retention time. For water bodies supplied by tap water, inorganic suspended solids and total phosphorus are more significant. Water bodies with a dual function of urban flood control should focus on dissolved oxygen, ammonium nitrogen, and chemical oxygen demand. Based on these findings, targeted management strategies are proposed to enhance algae management, control suspended solids input, and adjust water retention times, aiming to improve the transparency and quality of Taiyuan’s urban landscape.

Keywords: urban landscape lakes; water quality; water transparency; Sobol sensitivity analysis; Morris sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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