A spatially explicit interpretable machine-learning method to track dissolved inorganic nitrogen pollution in a coastal watershed

[Display omitted] •The DIN dynamics between the different types of sub-watersheds were identified using a spatially explicit interpretable machine learning method.•Soil moisture can strongly influence the riverine DIN concentration in the catchment, especially riverine nitrate.•Agricultural activiti...

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Bibliographic Details
Published in:Ecological indicators 2024-01, Vol.158, p.111428, Article 111428
Main Authors: Zhang, Zhenyu, Huang, Yicheng, Huang, Jinliang
Format: Article
Language:English
Subjects:
advection
artificial intelligence
climate
dissolved inorganic nitrogen
groundwater
Jiulong River watershed
Land use
Machine-learning
Nitrogen
nutrient management
pollution
population dynamics
riparian areas
rivers
soil
Spatially explicit
urbanization
water content
water management
watersheds
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