Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies

Accurate and sufficient water quality data is essential for watershed management and sustainability. Machine learning models have shown great potentials for estimating water quality with the development of online sensors. However, accurate estimation is challenging because of uncertainties related t...

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Veröffentlicht in:	PloS one Jg. 17; H. 7; S. e0271458
Hauptverfasser:	Chen, Shengyue, Zhang, Zhenyu, Lin, Juanjuan, Huang, Jinliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	San Francisco Public Library of Science 13.07.2022 Public Library of Science (PLoS)
Schlagworte:	Accuracy Algorithms Ammonia Analysis Artificial neural networks Back propagation networks Biology and Life Sciences Chemical oxygen demand Coastal waters Computer and Information Sciences Datasets Dissolved oxygen Earth Sciences Ecology and Environmental Sciences Electrical conductivity Electrical resistivity Environmental aspects Estimation Evaluation Hydrogen Hydrogen ion concentration Hydrogen ions Indicators Ion concentration Laboratories Learning algorithms Machine learning Management Modelling Monte Carlo simulation Neural networks Nitrogen Nutrient concentrations Nutrients Oxygen Phosphorus Physical Sciences Research and Analysis Methods Research methodology Sampling Security Sensors Support vector machines Sustainability Sustainable development Turbidity Uncertainty Water Water quality Water quality management Water security Water temperature Watershed management Watersheds China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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