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 this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.

• 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