Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia
- Autores:
-
Avila Vélez, Edier Fernando
Royero Benavides, Bibiana del Pilar
Herrera Matinez, Luis Alberto
Prada Castro, Yorley Milena
Méndez, Diego Orlando
Hernández Ortiz, Carlos Andrés
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/18751
- Acceso en línea:
- https://repositorio.uptc.edu.co/handle/001/18751
https://doi.org/10.19053/uptc.01217488.v15.n2.2024.16499
- Palabra clave:
- Teledetección, espectroradiometría, variables fisicoquímicas, calidad de agua.
Remote sensing, spectroradiometry, physicochemical variables, water quality.
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
| id |
REPOUPTC2_461cb4561786c8d02afbed7a19c0c1af |
|---|---|
| oai_identifier_str |
oai:repositorio.uptc.edu.co:001/18751 |
| network_acronym_str |
REPOUPTC2 |
| network_name_str |
RiUPTC: Repositorio Institucional UPTC |
| repository_id_str |
|
| dc.title.es-ES.fl_str_mv |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| title |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| spellingShingle |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia Teledetección, espectroradiometría, variables fisicoquímicas, calidad de agua. Remote sensing, spectroradiometry, physicochemical variables, water quality. |
| title_short |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| title_full |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| title_fullStr |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| title_full_unstemmed |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| title_sort |
Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca Colombia |
| dc.creator.fl_str_mv |
Avila Vélez, Edier Fernando Royero Benavides, Bibiana del Pilar Herrera Matinez, Luis Alberto Prada Castro, Yorley Milena Méndez, Diego Orlando Hernández Ortiz, Carlos Andrés |
| dc.contributor.author.spa.fl_str_mv |
Avila Vélez, Edier Fernando Royero Benavides, Bibiana del Pilar Herrera Matinez, Luis Alberto Prada Castro, Yorley Milena Méndez, Diego Orlando Hernández Ortiz, Carlos Andrés |
| dc.subject.none.fl_str_mv |
Teledetección, espectroradiometría, variables fisicoquímicas, calidad de agua. Remote sensing, spectroradiometry, physicochemical variables, water quality. |
| topic |
Teledetección, espectroradiometría, variables fisicoquímicas, calidad de agua. Remote sensing, spectroradiometry, physicochemical variables, water quality. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-12-11 00:00:00 2026-04-16T17:12:37Z |
| dc.date.available.none.fl_str_mv |
2024-12-11 00:00:00 |
| dc.date.issued.none.fl_str_mv |
2024-12-11 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.local.eng.fl_str_mv |
Journal article |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
publishedVersion |
| dc.identifier.issn.none.fl_str_mv |
0121-7488 |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.uptc.edu.co/handle/001/18751 |
| dc.identifier.doi.none.fl_str_mv |
10.19053/uptc.01217488.v15.n2.2024.16499 |
| dc.identifier.eissn.none.fl_str_mv |
2462-7658 |
| dc.identifier.url.none.fl_str_mv |
https://doi.org/10.19053/uptc.01217488.v15.n2.2024.16499 |
| identifier_str_mv |
0121-7488 10.19053/uptc.01217488.v15.n2.2024.16499 2462-7658 |
| url |
https://repositorio.uptc.edu.co/handle/001/18751 https://doi.org/10.19053/uptc.01217488.v15.n2.2024.16499 |
| dc.language.iso.none.fl_str_mv |
|
| language_invalid_str_mv |
|
| dc.relation.references.none.fl_str_mv |
A.Dourojeanni, A. Jouravlev, CEPAL Gestión de cuencas y ríos vinculados con centros urbanos [online]. CEPAL. 1999.Disponible en: https://bibliotecadigital.ciren.cl/handle/20.500.13082/33031 P.R. Kannel, S. Lee, S.R. Kanel, “Spatial–temporal variation and comparative assessment of water qualities of urban river system: a case study of the river Bagmati (Nepal)”, Environ Monit Assess, vol. 129, pp. 433–45, january 2007. https://doi.org/10.1007/s10661-006-9375-6 Ministerio de Ambiente, Vivienda y Desarrollo Territorial, Política Nacional para la Gestión Integral del Recurso Hídrico[online]. Bogotá, D.C.: Colombia, 2010 Disponible en: https://www.minambiente.gov.co/wp-content/uploads/2021/10/Politica-nacional-Gestion-integral-de-recurso-Hidrico-web.pdf M.H. Gholizadeh, A.M. Melesse, L.A. Reddi, “Comprehensive Review on Water Quality Parameters E stimation Using Remote Sensing Techniques”, Sensors vol. 16, pp. 12-98, agosto 2016. https://doi.org/10.3390/s16081298 N.E. Samboni, Y. Carvajal, J.C. Escobar, “Revisión de parámetros fisicoquímicos como indicadores de calidad y contaminación del agua” Ingeniería e investigación vol 27 no 3, pp. 172-181, diciembre 2007. https://www.redalyc.org/articulo.oa?id=64327320 B.A. Schaeffer, K.G. Schaeffer, D. Keith, R.S. Lunetta, R. Conmy, R.W. Gould, “Barriers to adopting satellite remote sensing for water quality management”,Int J Remote Sens, vol.34, pp. 7534-7544, Agosto 2013. https://doi.org/10.1080/01431161.2013.82354 R.H. Becker, M. Sayers, D. Dehm, R. Shuchman, K. Quintero, K. Bosse, R. Sawtell, " Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms: A new paradigm in water quality monitoring", Journal of Great Lakes Research, vol. 45 no 3, pp.444-53, june 2019. https://doi.org/10.1016/J.JGLR.2019.03.006 Y. Zhang, X. Kong, L. Deng, Y. Liu, Y. " Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River", Journal of Environmental Management, vol. 342, pp. 118283, september 2023. https://doi.org/10.1016/J.JENVMAN.2023.118283 S. Chen, L. Han, X. Chen, D. Li, L. Sun, Y. Li, " Estimating wide range Total Suspended Solids concentrations from MODIS 250-m imageries: An improved method. ISPRS", Journal of Photogrammetry and Remote Sensing, vol. 99, pp. 58–69, january 2015. https://doi.org/10.1016/J.ISPRSJPRS.2014.10.006 S. Imen, N. Chang, Bin, Y.J. Yang, " Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead". Journal of Environmental Management, vol. 160, pp. 73–89, september 2015. https://doi.org/10.1016/J.JENVMAN.2015.06.003 S. Chen, L. Fang, L. Zhang, W. Huang, " Remote sensing of turbidity in seawater intrusion reaches of Pearl River Estuary – A case study in Modaomen water way, China",. Estuarine, Coastal and Shelf Science, vol. 82 no1, pp. 119–127, march 2009. https://doi.org/10.1016/J.ECSS.2009.01.003 S.H. Rahat, T. Steissberg, W. Chang, X. Chen, G. Mandavya, J. Tracy, A. Wasti, G. Atreya, S. Saki, M.A. Bhuiyan, P. Ray, "Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty", Science of The Total Environment, vol. 898, pp. 165-504, noviembre 2023. https://doi.org/10.1016/J.SCITOTENV.2023.165504 M.G. Uddin, S. Nash, A. Rahman, A.I. Olbert, " A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment", Water Research, vol 219, 118532, july 2022. https://doi.org/10.1016/J.WATRES.2022.118532 M.L. Wu, Y.S. Wang, Y.T. Wang, J.P. Yin, J. Dong, Z.Y. Jiang, F.L. Sun, " Scenarios of nutrient alterations and responses of phytoplankton in a changing Daya Bay, South China Sea". Journal of Marine Systems, vol. 165, pp. 1–12, january 2017). https://doi.org/10.1016/J.JMARSYS.2016.09.004 R. Beck, S. Zhan, H. Liu, S. Tong, B. Yang, M. Xu, Z. Ye, Y. Huang, S. Shu, Q. Wu, S. Wang, K. Berling, A. Murray, E. Emery, M. Reif, J. Harwood, J. Young, C. Nietch, D. Macke, H. Su, " Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations",. Remote Sensing of Environment, vol. 178, pp. 15–30, june 2016. https://doi.org/10.1016/J.RSE.2016.03.002 J. Cai, L. Meng, H. Liu, J. Chen, Q Xing, " Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images", Ecological Indicators, vol. 139, 108936, june 2022. https://doi.org/10.1016/J.ECOLIND.2022.108936 Y. Qiu, H.E. Zhang, X. Tong, Y. Zhang, J. Zhao, Monitoring the water quality of water resources reservation area in upper region of Huangpu River using remote sensing[online]. IEEE International Symposium on Geoscience and Remote Sensing pp. 1082-1085, 2006 Disponible en: 10.1109/IGARSS.2006.279. X. Cai, Y. Li, S. Lei, S. Zeng, Z. Zhao, H. Lyu, X. Dong, J. Li, H. Wang, J. Xu, Y. Zhu, L. Wu, X. Cheng, "A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China". Science of The Total Environment, vol. 856, 158869. january 2023. https://doi.org/10.1016/J.SCITOTENV.2022.158869 S.H. Rahat, Y. Steissberg, W. Chang, X. Chen, G. Mandavya, J. Tracy, A. Wasti, G. Atreya, S. Saki, M.A. Bhuiyan, P. Ray, “Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty”, Science of The Total Environment, vol. 898, 165504, 2023 https://doi.org/10.1016/J.SCITOTENV.2023.165504 M.G. Uddin, S. Nash, A. Rahman, A.I. Olbert, “A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment”. Water Research, vol. 219, 118532, 2022. https://doi.org/10.1016/J.WATRES.2022.118532 C. Zhou, J. Zhang, "Simultaneous measurement of chemical oxygen demand and turbidity in water based on broad optical spectra using backpropagation neural network". Chemometrics and Intelligent Laboratory Systems, vol. 237, 104830, 202. https://doi.org/10.1016/J.CHEMOLAB.2023.104830 R. Chafloque, C. Rodriguez, Y. Pomachagua, M. Hilario, M. “Predictive Neural Networks Model for Detection of Water Quality for Human ConsumptionIn 2021” 13th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 172-176, september 2021. A.H. Haghiabi, A.H. Nasrolahi, A. Parsaie, “Water quality prediction using machine learning methods Water Quality”, Revista de investigación sobre la calidad del agua vol.53 no 1 pp. 3-13, 2018. https://doi.org/10.2166/wqrj.2018.025 A.C. Aguilar, F.F. Obando, “APRENDIZAJE AUTOMÁTICO PARA LA PREDICCIÓN DE CALIDAD DE AGUA POTABLE”, INGENIARE, vol. 28, pp. 59–74, 2020. https://doi.org/https://doi.org/10.18041/1909-2458/ingeniare.28.6215 M.F. Gutiérrez Fonseca, Modelo de gobernanza del agua en la Cuenca del Río Cuja [online]. Universidad UDCA, 2018 Disponible en: https://repository.udca.edu.co/handle/11158/963. Corporación Autónoma Regional CAR, Delimitación y localización de la cuenca Sumapaz y subcuenca río alto Sumapaz. En: CAR. Diagnóstico, prospectiva y formulación de la cuenca hidrográfica del río Sumapaz. Plan de Manejo y Ordenamiento de la Cuenca (POMCA) del río Sumapaz [online], Informe POMCA-002 UT. Bogotá DC: ed. CAR; 2016 Disponible en: https://www.car.gov.co/uploads/files/5ac68e4163931.pdf P. Chober, L.A. Schwarte, “Correlation coefficients: Appropriate use and interpretation”. Anesthesia and Analgesia, vol. 126, no 5, pp. 1763–1768, 2018. https://doi.org/10.1213/ANE.0000000000002864 J.L. Rodgers, W.A NiceWander, “Thirteen ways to look at the correlation coefficient”, American Statistician, vol. 42, No 1, pp. 59–66, 1988. https://doi.org/10.1080/00031305.1988.10475524 D. Chicco, M.J. Warrens, G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ” Computer Science, vol. 7, pp. 1–24, 2021. https://doi.org/10.7717/PEERJ-CS.623 J.D. Nagelkerke, “Miscellanea A note on a general definition of the coefficient of determination” Biometrika, Vol. 78, No 3, pp. 691-692, september, 1991. https://academic.oup.com/biomet/article-abstract/78/3/691/256225?redirectedFrom=fulltext&login=false J.L. Wu, C.R. Ho, C.C., Huang, A.L. Srivastav, J.H. Tzeng, Y.T. Lin, “Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids”. Sensors (Switzerland), vol. 14, No 12, pp. 22670–22688, 2014. https://doi.org/10.3390/s141222670. I. Won Seo, H. Yun, S.Y. Choi, “Forecasting Water Quality Parameters by ANN Model using Pre-processing Technique at The Downstream of Cheongpyeong Dam. Procedi” Engineering, vol, 154, pp. 1110–1115, 2016. https://doi.org/10.1016/j.proeng.2016.07.519 I. Melendez Pastor, J. Navarro Pedreño, I. Gómez, M. Koch, “Identifying optimal spectral bands to assess soil properties with VNIR radiometry in semi-arid soils”. Geoderma, vol. 147, No 3–4, pp. 126–132, 2008. https://doi.org/10.1016/j.geoderma.2008.08.004 J.D. Aguilar Sánchez, N. Cubas Irigoín, N. “Contaminación de suelos por el uso de aguas residuales”, Revista Alfa, vol. 5, No 14, pp. 132–144, 2021. https://doi.org/10.33996/revistaalfa.v5i14.104 J.W. Lopes, F.B. Lopes, E.M. Andrade, L.C.G. Chaves, M.G.R.Carneiro, “Spectral Response of Water Under Different Concentrations of Suspended Sediment: Measurement and Simplified Modeling”, Journal of Agricultural Science, vol. 11, No 3, pp. 327. 2019. https://doi.org/10.5539/jas.v11n3p327 S. Hafeez, M.S. Wong, S. Abbas, Y.T. Kwok, J. Nichol. K.H. Lee, D. Tang, L. Pun, “Detection and Monitoring of Marine Pollution Using Remote”, Sensing Technologies, 2018. https://books.google.com.co/books?hl=es&lr=&id=mTj8DwAAQBAJ&oi=fnd&pg=PA7&ots=Om7DvOxZnY&sig=wbnlh9UuyuqkcMaUIjQpI8k7b3s&redir_esc=y#v=onepage&q&f=false C. De La Hoz, C. Gotilla, MODELO PARA LA PREDICCION DE PARAMETROS DE CALIDAD DEL AGUA DEL LAGO DE VALENCIA UTILIZANDO IMÁGENES DE SATELITE [online], XIII Congreso de la Asociación Españolade teledetección 2009 Disponible en: http://www.aet.org.es/congresos/xiii/cal29.pdf J. Gallego Ayala, J. A. Gómez Limón, “Análisis de instrumentos políticos para el control de la contaminación por nitratos de la agricultura de regadío en Castilla y León (España)”. Spanish Journal of Agricultural Research, vol. 7, No 1, pp. 24–40, 2009, https://doi.org/10.5424/sjar/2009071-395 D. Wang, Q. Cui, F. Gong, L. Wang, X. He, Y. Bai, “Satellite retrieval of Surface wáter nutrients in the coastal regions of the east China Sea”. Remote Sensing, vol. 10, No 12, pp. 1896, noviembre, 2018. https://www.mdpi.com/2072-4292/10/12/1896 Q. Jiang, J. He, J. Wu, X. Hu, Y. Ye, G. Christakos, “Assessing the severe eutrophication status and spatial trend in the coastal waters of Zhejiang province (China)”. Limnology and oceonography, vol. 64, No 1, pp. 3-17, agosto, 2018. https://doi.org/10.1002/lno.11013 N.P. Kumudini, K. Sok, D.G. Avanthi, H. Yohey, C. Yoon, M. Raj, S. Binoy, S. Yong, “Fe(III) loaded chitosan-biochar composite fibers for the removal of phosphate from water”, Journal of Hazardous Materials, vol. 415, agosto, 2021. https://doi.org/10.1016/j.jhazmat.2021.125464. |
| dc.relation.citationendpage.none.fl_str_mv |
23 |
| dc.relation.citationissue.spa.fl_str_mv |
2 |
| dc.relation.citationstartpage.none.fl_str_mv |
15 |
| dc.relation.citationvolume.spa.fl_str_mv |
15 |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Ciencia en Desarrollo |
| dc.relation.bitstream.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/16499/14822 |
| dc.relation.citationedition.spa.fl_str_mv |
Núm. 2 , Año 2024 : Vol. 15 Núm. 2 (2024): Vol 15, Núm.2 (2024): Julio-Diciembre |
| dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Pedagógica y Tecnológica de Colombia |
| dc.source.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/16499 |
| institution |
Universidad Pedagógica y Tecnológica de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.uptc.edu.co/bitstreams/1d83be22-8a6d-4284-94b1-e21088783907/download |
| bitstream.checksum.fl_str_mv |
bebe1d02bd7a727ad782059e5bb1b507 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UPTC |
| repository.mail.fl_str_mv |
bdigital@metabiblioteca.com |
| _version_ |
1881673889841414144 |
| spelling |
Avila Vélez, Edier FernandoRoyero Benavides, Bibiana del PilarHerrera Matinez, Luis AlbertoPrada Castro, Yorley MilenaMéndez, Diego OrlandoHernández Ortiz, Carlos Andrés2024-12-11 00:00:002026-04-16T17:12:37Z2024-12-11 00:00:002024-12-110121-7488https://repositorio.uptc.edu.co/handle/001/1875110.19053/uptc.01217488.v15.n2.2024.164992462-7658https://doi.org/10.19053/uptc.01217488.v15.n2.2024.16499application/pdfUniversidad Pedagógica y Tecnológica de Colombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/16499Teledetección, espectroradiometría, variables fisicoquímicas, calidad de agua.Remote sensing, spectroradiometry, physicochemical variables, water quality.Diseño de un modelo correlacional para cuantificar variables fisicoquímicas a partir de técnicas de espectroradiometría en cuerpos de agua. Caso de Estudio Rio Cuja Cundinamarca ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85A.Dourojeanni, A. Jouravlev, CEPAL Gestión de cuencas y ríos vinculados con centros urbanos [online]. CEPAL. 1999.Disponible en: https://bibliotecadigital.ciren.cl/handle/20.500.13082/33031P.R. Kannel, S. Lee, S.R. Kanel, “Spatial–temporal variation and comparative assessment of water qualities of urban river system: a case study of the river Bagmati (Nepal)”, Environ Monit Assess, vol. 129, pp. 433–45, january 2007. https://doi.org/10.1007/s10661-006-9375-6Ministerio de Ambiente, Vivienda y Desarrollo Territorial, Política Nacional para la Gestión Integral del Recurso Hídrico[online]. Bogotá, D.C.: Colombia, 2010 Disponible en: https://www.minambiente.gov.co/wp-content/uploads/2021/10/Politica-nacional-Gestion-integral-de-recurso-Hidrico-web.pdfM.H. Gholizadeh, A.M. Melesse, L.A. Reddi, “Comprehensive Review on Water Quality Parameters E stimation Using Remote Sensing Techniques”, Sensors vol. 16, pp. 12-98, agosto 2016. https://doi.org/10.3390/s16081298N.E. Samboni, Y. Carvajal, J.C. Escobar, “Revisión de parámetros fisicoquímicos como indicadores de calidad y contaminación del agua” Ingeniería e investigación vol 27 no 3, pp. 172-181, diciembre 2007. https://www.redalyc.org/articulo.oa?id=64327320B.A. Schaeffer, K.G. Schaeffer, D. Keith, R.S. Lunetta, R. Conmy, R.W. Gould, “Barriers to adopting satellite remote sensing for water quality management”,Int J Remote Sens, vol.34, pp. 7534-7544, Agosto 2013. https://doi.org/10.1080/01431161.2013.82354R.H. Becker, M. Sayers, D. Dehm, R. Shuchman, K. Quintero, K. Bosse, R. Sawtell, " Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms: A new paradigm in water quality monitoring", Journal of Great Lakes Research, vol. 45 no 3, pp.444-53, june 2019. https://doi.org/10.1016/J.JGLR.2019.03.006Y. Zhang, X. Kong, L. Deng, Y. Liu, Y. " Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River", Journal of Environmental Management, vol. 342, pp. 118283, september 2023. https://doi.org/10.1016/J.JENVMAN.2023.118283S. Chen, L. Han, X. Chen, D. Li, L. Sun, Y. Li, " Estimating wide range Total Suspended Solids concentrations from MODIS 250-m imageries: An improved method. ISPRS", Journal of Photogrammetry and Remote Sensing, vol. 99, pp. 58–69, january 2015. https://doi.org/10.1016/J.ISPRSJPRS.2014.10.006S. Imen, N. Chang, Bin, Y.J. Yang, " Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead". Journal of Environmental Management, vol. 160, pp. 73–89, september 2015. https://doi.org/10.1016/J.JENVMAN.2015.06.003S. Chen, L. Fang, L. Zhang, W. Huang, " Remote sensing of turbidity in seawater intrusion reaches of Pearl River Estuary – A case study in Modaomen water way, China",. Estuarine, Coastal and Shelf Science, vol. 82 no1, pp. 119–127, march 2009. https://doi.org/10.1016/J.ECSS.2009.01.003S.H. Rahat, T. Steissberg, W. Chang, X. Chen, G. Mandavya, J. Tracy, A. Wasti, G. Atreya, S. Saki, M.A. Bhuiyan, P. Ray, "Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty", Science of The Total Environment, vol. 898, pp. 165-504, noviembre 2023. https://doi.org/10.1016/J.SCITOTENV.2023.165504M.G. Uddin, S. Nash, A. Rahman, A.I. Olbert, " A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment", Water Research, vol 219, 118532, july 2022. https://doi.org/10.1016/J.WATRES.2022.118532M.L. Wu, Y.S. Wang, Y.T. Wang, J.P. Yin, J. Dong, Z.Y. Jiang, F.L. Sun, " Scenarios of nutrient alterations and responses of phytoplankton in a changing Daya Bay, South China Sea". Journal of Marine Systems, vol. 165, pp. 1–12, january 2017). https://doi.org/10.1016/J.JMARSYS.2016.09.004R. Beck, S. Zhan, H. Liu, S. Tong, B. Yang, M. Xu, Z. Ye, Y. Huang, S. Shu, Q. Wu, S. Wang, K. Berling, A. Murray, E. Emery, M. Reif, J. Harwood, J. Young, C. Nietch, D. Macke, H. Su, " Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations",. Remote Sensing of Environment, vol. 178, pp. 15–30, june 2016. https://doi.org/10.1016/J.RSE.2016.03.002J. Cai, L. Meng, H. Liu, J. Chen, Q Xing, " Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images", Ecological Indicators, vol. 139, 108936, june 2022. https://doi.org/10.1016/J.ECOLIND.2022.108936Y. Qiu, H.E. Zhang, X. Tong, Y. Zhang, J. Zhao, Monitoring the water quality of water resources reservation area in upper region of Huangpu River using remote sensing[online]. IEEE International Symposium on Geoscience and Remote Sensing pp. 1082-1085, 2006 Disponible en: 10.1109/IGARSS.2006.279.X. Cai, Y. Li, S. Lei, S. Zeng, Z. Zhao, H. Lyu, X. Dong, J. Li, H. Wang, J. Xu, Y. Zhu, L. Wu, X. Cheng, "A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China". Science of The Total Environment, vol. 856, 158869. january 2023. https://doi.org/10.1016/J.SCITOTENV.2022.158869S.H. Rahat, Y. Steissberg, W. Chang, X. Chen, G. Mandavya, J. Tracy, A. Wasti, G. Atreya, S. Saki, M.A. Bhuiyan, P. Ray, “Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty”, Science of The Total Environment, vol. 898, 165504, 2023 https://doi.org/10.1016/J.SCITOTENV.2023.165504M.G. Uddin, S. Nash, A. Rahman, A.I. Olbert, “A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment”. Water Research, vol. 219, 118532, 2022. https://doi.org/10.1016/J.WATRES.2022.118532C. Zhou, J. Zhang, "Simultaneous measurement of chemical oxygen demand and turbidity in water based on broad optical spectra using backpropagation neural network". Chemometrics and Intelligent Laboratory Systems, vol. 237, 104830, 202. https://doi.org/10.1016/J.CHEMOLAB.2023.104830R. Chafloque, C. Rodriguez, Y. Pomachagua, M. Hilario, M. “Predictive Neural Networks Model for Detection of Water Quality for Human ConsumptionIn 2021” 13th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 172-176, september 2021.A.H. Haghiabi, A.H. Nasrolahi, A. Parsaie, “Water quality prediction using machine learning methods Water Quality”, Revista de investigación sobre la calidad del agua vol.53 no 1 pp. 3-13, 2018. https://doi.org/10.2166/wqrj.2018.025A.C. Aguilar, F.F. Obando, “APRENDIZAJE AUTOMÁTICO PARA LA PREDICCIÓN DE CALIDAD DE AGUA POTABLE”, INGENIARE, vol. 28, pp. 59–74, 2020. https://doi.org/https://doi.org/10.18041/1909-2458/ingeniare.28.6215M.F. Gutiérrez Fonseca, Modelo de gobernanza del agua en la Cuenca del Río Cuja [online]. Universidad UDCA, 2018 Disponible en: https://repository.udca.edu.co/handle/11158/963.Corporación Autónoma Regional CAR, Delimitación y localización de la cuenca Sumapaz y subcuenca río alto Sumapaz. En: CAR. Diagnóstico, prospectiva y formulación de la cuenca hidrográfica del río Sumapaz. Plan de Manejo y Ordenamiento de la Cuenca (POMCA) del río Sumapaz [online], Informe POMCA-002 UT. Bogotá DC: ed. CAR; 2016 Disponible en: https://www.car.gov.co/uploads/files/5ac68e4163931.pdfP. Chober, L.A. Schwarte, “Correlation coefficients: Appropriate use and interpretation”. Anesthesia and Analgesia, vol. 126, no 5, pp. 1763–1768, 2018. https://doi.org/10.1213/ANE.0000000000002864J.L. Rodgers, W.A NiceWander, “Thirteen ways to look at the correlation coefficient”, American Statistician, vol. 42, No 1, pp. 59–66, 1988. https://doi.org/10.1080/00031305.1988.10475524D. Chicco, M.J. Warrens, G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ” Computer Science, vol. 7, pp. 1–24, 2021. https://doi.org/10.7717/PEERJ-CS.623J.D. Nagelkerke, “Miscellanea A note on a general definition of the coefficient of determination” Biometrika, Vol. 78, No 3, pp. 691-692, september, 1991. https://academic.oup.com/biomet/article-abstract/78/3/691/256225?redirectedFrom=fulltext&login=falseJ.L. Wu, C.R. Ho, C.C., Huang, A.L. Srivastav, J.H. Tzeng, Y.T. Lin, “Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids”. Sensors (Switzerland), vol. 14, No 12, pp. 22670–22688, 2014. https://doi.org/10.3390/s141222670.I. Won Seo, H. Yun, S.Y. Choi, “Forecasting Water Quality Parameters by ANN Model using Pre-processing Technique at The Downstream of Cheongpyeong Dam. Procedi” Engineering, vol, 154, pp. 1110–1115, 2016. https://doi.org/10.1016/j.proeng.2016.07.519I. Melendez Pastor, J. Navarro Pedreño, I. Gómez, M. Koch, “Identifying optimal spectral bands to assess soil properties with VNIR radiometry in semi-arid soils”. Geoderma, vol. 147, No 3–4, pp. 126–132, 2008. https://doi.org/10.1016/j.geoderma.2008.08.004J.D. Aguilar Sánchez, N. Cubas Irigoín, N. “Contaminación de suelos por el uso de aguas residuales”, Revista Alfa, vol. 5, No 14, pp. 132–144, 2021. https://doi.org/10.33996/revistaalfa.v5i14.104J.W. Lopes, F.B. Lopes, E.M. Andrade, L.C.G. Chaves, M.G.R.Carneiro, “Spectral Response of Water Under Different Concentrations of Suspended Sediment: Measurement and Simplified Modeling”, Journal of Agricultural Science, vol. 11, No 3, pp. 327. 2019. https://doi.org/10.5539/jas.v11n3p327S. Hafeez, M.S. Wong, S. Abbas, Y.T. Kwok, J. Nichol. K.H. Lee, D. Tang, L. Pun, “Detection and Monitoring of Marine Pollution Using Remote”, Sensing Technologies, 2018. https://books.google.com.co/books?hl=es&lr=&id=mTj8DwAAQBAJ&oi=fnd&pg=PA7&ots=Om7DvOxZnY&sig=wbnlh9UuyuqkcMaUIjQpI8k7b3s&redir_esc=y#v=onepage&q&f=falseC. De La Hoz, C. Gotilla, MODELO PARA LA PREDICCION DE PARAMETROS DE CALIDAD DEL AGUA DEL LAGO DE VALENCIA UTILIZANDO IMÁGENES DE SATELITE [online], XIII Congreso de la Asociación Españolade teledetección 2009 Disponible en: http://www.aet.org.es/congresos/xiii/cal29.pdfJ. Gallego Ayala, J. A. Gómez Limón, “Análisis de instrumentos políticos para el control de la contaminación por nitratos de la agricultura de regadío en Castilla y León (España)”. Spanish Journal of Agricultural Research, vol. 7, No 1, pp. 24–40, 2009, https://doi.org/10.5424/sjar/2009071-395D. Wang, Q. Cui, F. Gong, L. Wang, X. He, Y. Bai, “Satellite retrieval of Surface wáter nutrients in the coastal regions of the east China Sea”. Remote Sensing, vol. 10, No 12, pp. 1896, noviembre, 2018. https://www.mdpi.com/2072-4292/10/12/1896Q. Jiang, J. He, J. Wu, X. Hu, Y. Ye, G. Christakos, “Assessing the severe eutrophication status and spatial trend in the coastal waters of Zhejiang province (China)”. Limnology and oceonography, vol. 64, No 1, pp. 3-17, agosto, 2018. https://doi.org/10.1002/lno.11013N.P. Kumudini, K. Sok, D.G. Avanthi, H. Yohey, C. Yoon, M. Raj, S. Binoy, S. Yong, “Fe(III) loaded chitosan-biochar composite fibers for the removal of phosphate from water”, Journal of Hazardous Materials, vol. 415, agosto, 2021. https://doi.org/10.1016/j.jhazmat.2021.125464.2321515Ciencia en Desarrollohttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/16499/14822Núm. 2 , Año 2024 : Vol. 15 Núm. 2 (2024): Vol 15, Núm.2 (2024): Julio-DiciembrePublicationOREORE.xmltext/xml3124https://repositorio.uptc.edu.co/bitstreams/1d83be22-8a6d-4284-94b1-e21088783907/downloadbebe1d02bd7a727ad782059e5bb1b507MD51falseAnonymousREAD001/18751oai:repositorio.uptc.edu.co:001/187512026-10-30 07:00:54.016https://creativecommons.org/licenses/by-nc-nd/4.0/deed.esmetadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com |