Cargando…
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 principales: | 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 |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036609/ https://www.ncbi.nlm.nih.gov/pubmed/33805544 http://dx.doi.org/10.3390/s21072374 |
Ejemplares similares
-
Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
por: Ariza Colpas, Paola, et al.
Publicado: (2020) -
XIII National Congress SISMES
Publicado: (2023) -
Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques
por: Patiño-Saucedo, Janns Alvaro, et al.
Publicado: (2022) -
Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review
por: Barrios-Ulloa, Alexis, et al.
Publicado: (2022) -
XII National Congress SISMES Padua, 8–10 October, 2021
Publicado: (2022)