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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Yeison Alberto Garcés-Gómez
Universidad Católica de Manizales
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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).
Bibliometrics on the use of remote sensing and machine learning in crop classification Andrea del Pilar Sánchez-Chavez; Vladimir Henao-Cespedes; Yeison Alberto Garcés-Gómez
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10476

Abstract

This study presents a bibliometric analysis of global research on crop classification using remote sensing and machine learning (ML), a field critical to advancing precision agriculture. A systematic search in Scopus identified 2,122 peer-reviewed articles published between 2014 and 2023. The analysis employed VOSviewer and the Bibliometrix package in R to assess publication trends, citation impact, and keyword co-occurrence networks. Results reveal a marked increase in scientific production after 2017, coinciding with the availability of high-resolution satellite imagery and the adoption of deep learning algorithms, particularly convolutional neural networks (CNNs). China emerged as the leading contributor, followed by the United States and India, reflecting strong investments in agricultural modernization and remote sensing infrastructure. Thematic mapping highlights both traditional research areas, such as vegetation indices and land cover classification, and emerging themes, including AI-supported algorithms and food security. Despite this growth, disparities persist, with most countries contributing fewer than 100 publications, underscoring the need to promote participation in underrepresented regions. Findings demonstrate the field’s rapid evolution, emphasize the integration of AI-driven methods in crop monitoring, and suggest future directions combining remote sensing, ML, and internet of things (IoT) technologies to address global challenges in food security and sustainable agricultural management.
Remote sensing in the analysis of the behavior of CO associated with confinement due to COVID-19, in the city of Manizales Vladimir Henao-Céspedes; Yeison Alberto Garcés-Gómez; Oscar Cardona-Morales
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7441

Abstract

This article analyzed the behavior of carbon monoxide (CO) levels in Manizales during pre-lockdown, lockdown, and post-lockdown, as a response to the coronavirus disease (COVID-19) pandemic. The analysis focuses on the data of CO levels obtained from the tropospheric monitoring instrument (TROPOMI), precipitation, and temperature (T) recorded by the network of stations of Caldas. The data allowed us to find that during the lockdown, the average value of CO was 9.92% lower than the value registered before the lockdown, and it was 11.75% lower after the lockdown. On the other hand, the correlation between CO levels and population density during the three periods was analyzed, obtaining an R^2=0.816 after lockdown. Finally, considering other possible variables that can affect the CO levels, an analysis of the behavior of CO was carried out concerning the temperature and precipitation of the city registered before, during, and after the lockdown. Regarding CO and temperature, the correlation was inverse with Pearson’s r=-0.599 (Fisher’s z=-0.692), which also supports the decreasing trend of the value measured, and that the variation of CO levels does not depend only on lockdown but also on other factors. Regarding CO and precipitation, a positive correlation of Pearson’s r =0.165 (Fisher’s z=0.167) was obtained.
Co-Authors Andrea del Pilar Sánchez-Chavez Angela Viviana Alzate-García Angie Marcela Galvez Valencia Carlos Augusto Gonzalez-Correa Clara Helena Gonzalez-Correa Diana Carolina Meneses-Cabezas Erwin Leandro Lemus Rodriguez Fredy E. Hoyos Gloria Yaneth Florez-Yepes Jesus Emilio Pinto-Lopera Jorge Luís Torres-León Jose Israel Cardenas-Jimenez Jose Manuel Monsalve-Tellez Julio Cesar Anaya-Valenzuela Luis Fernando Diaz-Cadavid María Nancy Marín Olaya Miguel Andres Arango Argoti Nicolás Toro-García Nicolás Toro-García Nicolás Toro-García Oscar Cardona-Morales Paula Andrea Duque Paula Andrea López-Jimenez Ruben Darío Lara-Escobar Vladimir Henao-Cespedes Yuliana Zapata-Serna