Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning

• Published in: Water research (Oxford) Vol. 284; p. 123900
• Main Authors: Zhang, Zhe-Xuan, Huguet, Arnaud, Hayet, Zoé, Parlanti, Edith
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.09.2025; IWA Publishing/Elsevier
• Subjects: Computer Science; DOM; Earth Sciences; Environmental Engineering; Environmental Monitoring - methods; Environmental Sciences; Estuaries; Fluorescence; France; Land use; Land-ocean continuum; Machine Learning; Oceanography; Organic Chemicals; Sciences of the Universe; Signal and Image Processing; Spectrometry, Fluorescence; Supervised Machine Learning; Water Pollutants, Chemical; France; Fluorescence; DOM; Land-ocean continuum; Land use; Machine learning

•ML captures dominant DOM optical parameters in different zones and scenarios.•Biogeochemical insights from explainable artificial intelligence.•Identification of zones that require attention to guide watershed management.•Establishment of a workflow to differentiate DOM composition in estuaries. Di...