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Evaluation of artificial intelligence algorithms to estimate water quality parameters using satellite images

IAES International Journal of Artificial Intelligence · Vol. 15 No. 1 (2026) · hlm. 559–567

DOI
10.11591/ijai.v15.i1.pp559-567
ISSN
2252-8938, 2089-4872
Terbit
2026-02-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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).

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  • Universidad Católica de Manizales
  • Universidad Nacional de Colombia
  • Universidad de Caldas
  • Universidad del Cauca
  • Universidad de Manizales
  • Universidad de la Amazonia
  • Nigerian Institute For Oil Palm Reasearch
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Vladimir Henao-CéspedesUniversidad Católica de Manizales7
Fredy E. HoyosUniversidad Nacional de Colombia6
Nicolás Toro GarcíaUniversidad Nacional de Colombia5
Alejandro RincónUniversidad Católica de Manizales2
Rubén Darío Lara-EscobarUniversidad Católica de Manizales2
Paula Andrea López JimenezUniversidad Católica de Manizales2
Eduardo Javid Corpas IguaránUniversidad Católica de Manizales2
Omar Alberto Tapasco AlzateUniversidad de Caldas2
LUIS FERNANDO DIAZ CADAVIDUniversidad Nacional de Colombia1
Angie Marcela Galvez ValenciaAfiliasi tidak diketahui1
Erwin Leandro Lemus RodriguezAfiliasi tidak diketahui1
Miguel Andres Arango ArgotiAfiliasi tidak diketahui1
Juan Carlos Granobles ToresUniversidad de Manizales1
José Israel Cardenas-JimenezUniversidad Nacional de Colombia1
Carlos-Augusto González-CorreaUniversidad de Caldas1
Clara Helena González-CorreaUniversidad de Caldas1
María Nancy Marín OlayaUniversidad Católica de Manizales1
Jesús Emilio Pinto LoperaUniversidad de la Amazonia1
Diana Carolina Meneses-CabezasUniversidad Católica de Manizales1
Yuliana Zapata-SernaUniversidad Católica de Manizales1
Jose Manuel Monsalve-TellezUniversidad Católica de Manizales1
Jorge Luís Torres-LeónNigerian Institute For Oil Palm Reasearch1
Christian Javier Pinza-JiménezUniversidad Católica de Manizales1
Astrid Lorena Urbano-CanoUniversidad del Cauca1
Diana Jimena López MesaCorporación Universitaria Comfacauca - Unicomfacauca1
Rosa Elvira Álvarez RoseroUniversidad del Cauca1
William Efrén Chirán-AlpalaUniversidad Católica de Manizales1
Lorena Paola Cárdenas EspinosaUniversidad Católica de Manizales1
Diego Alexander Gómez-MoyaAfiliasi tidak diketahui1
Laura Valeria Avendaño-GarcíaUniversidad Católica de Manizales1
Cristian Gómez-GuzmánUniversidad Católica de Manizales1
Erick Salvador Reyes-GalvánUniversidad Católica de Manizales1
Fredy Alexander Bolivar-GomezAfiliasi tidak diketahui1
Gloria Matilde Gil DuqueUniversidad Católica de Manizales1
Jessica Triviño-ValenciaUniversidad Católica de Manizales1
Jaime Alberto Giraldo-GarcíaUniversidad Nacional de Colombia1
Claudia Nohemy Montoya‐EstradaUniversidad Católica de Manizales1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Evaluation of artificial intelligence algorithms to estimate water quality parameters using satellite images</dc:title>
	<dc:creator>Anaya-Valenzuela, Julio Cesar</dc:creator>
	<dc:creator>Florez-Yepes, Gloria Yaneth</dc:creator>
	<dc:creator>Garcés-Gómez, Yeison Alberto</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Deep learning; Machine learning; Remote sensing; Spectral signature; Water quality</dc:subject>
	<dc:description xml:lang="en-US">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).</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-02-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/26759</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i1.pp559-567</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 1: February 2026; 559-567</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/26759/14961</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Julio Cesar Anaya-Valenzuela, Gloria Yaneth Florez-Yepes, Yeison Alberto Garcés-Gómez</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>