Mostrar el registro completo del ítem
Kadinski, L.; Schuster, J.; Abhijith, G.; Hao, C.; Grieb, A.; Meier, T.; Li, P.... (2024). Machine learning methodologies to predict possible water quality anomalies as a support tool for online monitoring of organic parameters. Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.14703
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/205984
Título: | Machine learning methodologies to predict possible water quality anomalies as a support tool for online monitoring of organic parameters | |
Autor: | Kadinski, Leonid Schuster, Jonas Abhijith, Gopinathan Hao, Cao Grieb, Anissa Meier, Thomas Li, Pu Ernst, Mathias Ostfeld, Avi | |
Fecha difusión: |
|
|
Resumen: |
[EN] Water Distribution Systems (WDSs) function to deliver high-quality water in major quantities. While standard water quality parameters are monitored at waterworks, it is still a challenge to monitor water quality in ...[+]
|
|
Palabras clave: |
|
|
Derechos de uso: | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | |
ISBN: |
|
|
Fuente: |
|
|
DOI: |
|
|
Editorial: |
|
|
Versión del editor: | http://ocs.editorial.upv.es/index.php/WDSA-CCWI/WDSA-CCWI2022/paper/view/14703 | |
Título del congreso: |
|
|
Lugar del congreso: |
|
|
Fecha congreso: |
|
|
Tipo: |
|