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...
- 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