SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of...

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Autores:
Ariza Colpas, Paola Patricia
Ayala Mantilla, Cristian Eduardo
Shaheen, Qaisar
Piñeres-Melo, Marlon Alberto
Villate-Daza, Diego Andrés
Morales-Ortega, Roberto Cesar
De-La-Hoz-Franco, Emiro
Sanchez Moreno, Hernando
Aziz, Butt Shariq
AFzal, Mehtab
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8323
Acceso en línea:
https://hdl.handle.net/11323/8323
https://doi.org/10.3390/s21072374
https://repositorio.cuc.edu.co/
Palabra clave:
IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
Rights
openAccess
License
CC0 1.0 Universal