Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Gloria Yaneth Florez-Yepes
Universidad Católica de Manizales
Computer Science & IT Electrical & Electronics Engineering Engineering

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Status index of optimal water variables for biodiversity conservation in the Lagoon of Sonso in Colombia Gloria Yaneth Florez-Yepes; Alejandro Rincón; Vladimir Henao Céspedes; Juan Carlos Granobles Tores; Fredy Edimer Hoyos Velasco
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp552-559

Abstract

In order to determine an index of the status of optimal water variables for biodiversity conservation in the Sonso Lagoon, the data obtained from 2004 to 2018 were used. To determine the index, a methodology based on a multivariate analysis of the physical-chemical water variables was used, as well as a correlation analysis for their delimitation. Subsequently, the definition of weights and the parameterization of the variables for the final construction of the index were made. As a result, it was found that the lagoon is in an adequate state with a value index of 0.65, with a highly vulnerable tendency to be in an acceptable state and go to a critical state, depending on the anthropic pressure it has. As a conclusion, it was obtained that variables such as dissolved oxygen, total phosphorus and electrical conductivity are determining factors in establishing the index.
Evaluation of artificial intelligence algorithms to estimate water quality parameters using satellite images Julio Cesar Anaya-Valenzuela; Gloria Yaneth Florez-Yepes; Yeison Alberto Garcés-Gómez
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp559-567

Abstract

The Ciénaga de la Virgen (Virgen Swamp) is a coastal lagoon in Cartagena de Indias that provides multiple ecosystem services in northern Bolívar. This ecosystem has faced anthropogenic pressure from city growth and improper water resource management, including wastewater and agrochemical discharges. Consequently, environmental authorities must monitor certain sites within the water body and extrapolate the data across its entire expanse. In this study, predictive tools are applied to determine water quality parameters such as chlorophyll-a (CL-a), dissolved oxygen (DO), total suspended solids (TSS), and salinity. This is achieved by correlating traditionally obtained data with the spectral response of medium-resolution satellite images, adjusted using artificial intelligence (AI) algorithms. Support vector machine (SVM) algorithms were used for regression, random forests (RF), and artificial neural networks (ANN), achieving an accuracy of 79% for CL-a, 95% for DO, 89% for TSS, and 96% for salinity. Validation was performed using mean absolute percentage error (MAPE) statistical metrics and root mean square error (RMSE).
Co-Authors Alejandro Rincón Fredy Edimer Hoyos Velasco Juan Carlos Granobles Tores Julio Cesar Anaya-Valenzuela Vladimir Henao Céspedes Yeison Alberto Garcés-Gómez