Identifying the drivers of chlorophyll-a dynamics in a landscape lake recharged by reclaimed water using interpretable machine learning

Chenchen Wang, Juan Liu, Chunsheng Qiu*, Xiao Su, Ning Ma, Jing Li, Shaopo Wang, Shen Qu*

*此作品的通讯作者

    科研成果: 期刊稿件文章同行评审

    11 引用 (Scopus)

    摘要

    The water quality of lakes recharged by reclaimed water is affected by both the fluctuation of reclaimed water quality and the biochemical processes in the lakes, and therefore the main controlling factors of algal blooms are difficult to identify. Taking a typical landscape lake recharged by reclaimed water as an example and using the spatiotemporal distribution characteristics and correlation analysis of water quality indexes, we propose an interpretable machine learning framework based on random forest to predict chlorophyll-a (Chl-a). The model considered nutrient difference indexes between reclaimed water and lake water, and further used feature importance ranking and partial dependence plot to identify nutrient drivers. Results show that the NO3-N input from reclaimed water is the dominant nutrient driver for algal bloom especially at high temperatures, and the negative correlation between NO3-N and Chl-a in the lake water is the consequence of algal bloom rather than the cause. Our study provides new insights into the identification of eutrophication factors for lakes recharged by reclaimed water.

    源语言英语
    文章编号167483
    期刊Science of the Total Environment
    906
    DOI
    出版状态已出版 - 1 1月 2024

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