DOI:
https://doi.org/10.14483/22484728.18391Published:
2018-08-13Issue:
Vol. 1 No. 2 (2018): Special editionSection:
A Case-Study VisionEstimation of conductivity in hydraulic affluents through self-organizing maps (SOM)
Predicción de la conductividad en afluentes hídricas mediante mapas auto-organizativos (SOM)
Keywords:
Redes neuronales artificiales, Minería de datos, Aprendizaje computacional, Mapas auto-organizativos (SOM), Conductividad en afluentes hídricas, Calidad del agua (es).Keywords:
Artificial neural networks, Data mining, Machine learning, Self-organizing maps (SOM), Water conductivity, Water quality (en).Downloads
Abstract (en)
This paper shows the use of self-organizing maps applied to water tributaries prediction. Currently, the environment conservation and the efficient water use, are pretty relevant issues. However, the water quality is not easy to measure, due to the specialized equipment for determining parameters such us: total coliforms, PH and dissolved oxygen. To measure the water conductivity, it was used a database with parameters of water quality, through the correlation of many involved parameters in water quality.
Abstract (es)
En este artículo se presenta el uso de mapas auto-organizativos aplicados a la predicción de afluentes hídricas. Actualmente, uno de los principales problemas es la preservación del medio ambiente y el uso eficiente del recurso hídrico. Sin embargo, la medición de la calidad del agua no es una tarea fácil, debido a lo especializado de los equipos para la determinación de parámetros tales como: coliformes totales, PH y oxígeno disuelto. Se empleó una base de datos con parámetros de calidad del agua para calcular la conductividad de la misma, a través de la correlación de varios parámetros involucrados en la calidad del agua.
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