Publications

Song, JL; Jiang, WS; Xin, L; Zhang, XQ (2024). Predicting the temporal-spatial distribution of chlorophyll-a in the Yellow River estuary using explainable machine learning. ESTUARINE COASTAL AND SHELF SCIENCE, 304, 108820.

Abstract
The water quality in estuaries is influenced by both physical and biochemical processes, making it difficult to identify and quantify the main controlling factors of chlorophyll -a (Chl-a) concentration. This study introduces an interpretable machine learning approach to identify and quantify the dominant control factors determining Chl-a concentration in the Yellow River Estuary (YRE). The model utilizes in situ data and MODIS data in the Yellow River Estuary, incorporating five key variables: salinity, water depth, turbidity, dissolved inorganic nitrogen (DIN), and soluble reactive phosphorus (SRP). Three types of inputs are used to predict Chl-a, including physical, biogeochemical, and physical-biogeochemical factors. The model further uses feature importance ranking and partial dependence plot to identify and quantify key control factors. The results reveal that (1) Chl-a variation is influenced by multiple factors, especially salinity and turbidity; (2) Chl-a concentration is inhibited when salinity is below 26, turbidity is lower than 16 NTU and above 30 NTU, DIN is lower than 0.314 mg/L and SRP is below 0.01 mg/L; (3) riverine inputs change turbidity and salinity in the Yellow River Estuary and Laizhou Bay, in response to temporal -spatial distributions of phytoplankton. This research can provide a valuable reference for water quality management in the Yellow River Estuary.

DOI:
10.1016/j.ecss.2024.108820

ISSN:
1096-0015