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Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation
High-frequency monitoring of water quality in catchments brings along the challenge of post-processing large amounts of data. Moreover, monitoring stations are often remote and technical issues resulting in data gaps are common. Machine learning algorithms can be applied to fill these gaps, and to a...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299926/ https://www.ncbi.nlm.nih.gov/pubmed/37368078 http://dx.doi.org/10.1007/s10661-023-11519-9 |