Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia

Maritime safety has become a relevant aspect in logistics processes using rivers. In Colombia, specifically in the Caribbean Region, there is the Magdalena River, a body of water that broadly borders the Colombian territory and is a tributary of various economic and public health activities. At its...

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Autores:
Paola Patricia, Ariza-Colpas
Ayala-Mantilla, Cristian Eduardo
Piñeres-Melo, Marlon-Alberto
Villate-Daza, Diego
Morales-Ortega, Roberto Cesa
De-la-Hoz Franco, Emiro
Sanchez-Moreno, Hernando
Butt Aziz, Shariq
Collazos Morales, Carlos
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8806
Acceso en línea:
https://hdl.handle.net/11323/8806
https://doi.org/10.1007/978-3-030-84340-3_19
https://repositorio.cuc.edu.co/
Palabra clave:
IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
Multilayer Preceptron
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_2070d5f4dc889ddc537586d259df4374
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8806
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
title Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
spellingShingle Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
Multilayer Preceptron
title_short Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
title_full Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
title_fullStr Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
title_full_unstemmed Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
title_sort Multilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - Colombia
dc.creator.fl_str_mv Paola Patricia, Ariza-Colpas
Ayala-Mantilla, Cristian Eduardo
Piñeres-Melo, Marlon-Alberto
Villate-Daza, Diego
Morales-Ortega, Roberto Cesa
De-la-Hoz Franco, Emiro
Sanchez-Moreno, Hernando
Butt Aziz, Shariq
Collazos Morales, Carlos
dc.contributor.author.spa.fl_str_mv Paola Patricia, Ariza-Colpas
Ayala-Mantilla, Cristian Eduardo
Piñeres-Melo, Marlon-Alberto
Villate-Daza, Diego
Morales-Ortega, Roberto Cesa
De-la-Hoz Franco, Emiro
Sanchez-Moreno, Hernando
Butt Aziz, Shariq
Collazos Morales, Carlos
dc.subject.spa.fl_str_mv IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
Multilayer Preceptron
topic IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
Multilayer Preceptron
description Maritime safety has become a relevant aspect in logistics processes using rivers. In Colombia, specifically in the Caribbean Region, there is the Magdalena River, a body of water that broadly borders the Colombian territory and is a tributary of various economic and public health activities. At its mouth, this river interacts with the sea directly, which generates a phenomenon called saline wedge, which is directly related to the sediments that must be continuously extracted and which threatens the proper functioning of the port from the city of Barranquilla, Colombia. Through this research, a network of sensors located in strategic places at the mouth of this river was generated, which allows predicting the behavior of the salt wedge. Using artificial neural networks, more specifically, the Multilayer Perceptron algorithm, it was possible to analyze the results of the implementation in light of the indicators or quality metrics, generating a highly reliable scenario that can be replicated in other sections of the river and in other aquifers.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-10-26T19:45:50Z
dc.date.available.none.fl_str_mv 2021-10-26T19:45:50Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-030-84340-3_19
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/8806
https://doi.org/10.1007/978-3-030-84340-3_19
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identifier_str_mv Corporación Universidad de la Costa
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language eng
dc.relation.references.spa.fl_str_mv Kellog, J.: Cenozoic tectonic history of the Sierra de Perijá, Venezuela-Colombia, and adjacent basins. Geol. Soc. Am. Mem. 162, 239–261 (1984)
Restrepo, J.D., Kjerfve, B.: Magdalena river: interannual variability (1975–1995) and revised water discharge and sediment load estimates. J. Hydrol. 235, 137–149 (2000)
Restrepo, J.D., Kjerfve, B.: The pacific and Caribbean rivers of Colombia: water discharge, sediment transport and dissolved loads. In: Lacerda, L., Santelli, R., Duursma, E., Abrao, J. (eds.) Environmental Geochemistry in Tropical and Subtropical Environments, pp. 169–187. Springer, Berlín (2004). https://doi.org/10.1007/978-3-662-07060-4_14
Restrepo, J.D.: Applicability of LOICZ catchment-coast continuum in a major Caribbean basin: the Magdalena River, Colombia. Estuar. Coast. Shelf Sci. 77, 214–229 (2008)
Restrepo, J.C., Otero, L., Lopez, S.: Clima de oleaje en el Pacifico sur de Colombia, delta del Río de Mira: Comparaciones Estadísticas y Aplicación a procesos Costeros. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales. 128(33), 339–357 (2009)
Bhattacharya, B., Price, R.K., Solomatine, D.P.: Machine learning approach to modeling sediment transport. J. Hydraul. Eng. 133(4), 440–450 (2007)
Fisher, L.H.: Sediment dynamics in the Magdalena river basin, Colombia: implications for understanding tropical river processes and hydropower development (2020)
Alizamir, M., et al.: Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS ONE 15(4), e0231055 (2020)
Li, Y., Wang, X., Zhao, Z., Han, S., Liu, Z.: Lagoon water quality monitoring based on digital image analysis and machine learning estimators. Water Res. 172, 115471 (2020)
Alfonso, L., Tefferi, M.: Effects of uncertain control in transport of water in a river-wetland system of the Low Magdalena River, Colombia. In: Ocampo-Martinez, C., Negenborn, R.R. (eds.) Transport of water versus transport over water, pp. 131–144. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16133-4_8
Ren, J., et al.: Multi-objective optimization of wave break forest design through machine learning. J. Hydroinf. 21(2), 295–307 (2019)
Anfuso, G., Rangel-Buitrago, N., Arango, I.D.C.: Evolution of sandspits along the Caribbean coast of Colombia: natural and human influences. In: Randazzo, G., Jackson, D.W.T., Andrew, J., Cooper, G. (eds.) Sand and Gravel Spits, pp. 1–19. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13716-2_1
Phillips, A.: Modelling riverine dissolved silica on different spatial and ttemporal scales using statistical and machine learning methods. Doctoral dissertation (2020)
Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., Ghalhari, G.A.F.: Machine learning to estimate surface soil moisture from remote sensing data. Water 12(11), 3223 (2020)
Björk, K.-M., Eirola, E., Miche, Y., Lendasse, A.: A new application of machine learning in health care, pp. 1–4 (2016). https://doi.org/10.1145/2910674.2935861
Ariza Colpas, P., Vicario, E., De-La-Hoz-Franco, E., Pineres-Melo, M., Oviedo-Carrascal, A., Patara, F.: Unsupervised human activity recognition using the clustering approach: a review. Sensors 20(9), 2702 (2020)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Amiribesheli, M., Benmansour, A., Bouchachia, A.: A review of smart homes in healthcare. J. Ambient. Intell. Humaniz. Comput. 6(4), 495–517 (2015). https://doi.org/10.1007/s12652-015-0270-2
Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf Technol. Biomed. 14(2), 274–283 (2010). https://doi.org/10.1109/TITB.2009.203731
McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, no. 1, pp. 41–48, July 1998
Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14(9), 755–763 (1998). https://academic.oup.com/bioinformatics/article-abstract/14/9/755/259550. Envejecimiento y salud (5 February 2018). https://www.who.int/es/news-room/fact-sheets/detail/envejecimiento-y-salud
Murata, N., Yoshizawa, S., Amari, S.: Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Networks 5(6), 865–872 (1994). https://doi.org/10.1109/72.329683
Du, W.S., Hu, B.Q.: Approximate distribution reducts in inconsistent interval-valued ordered decision tables. Inf. Sci. 271, 93–114 (2014). https://doi.org/10.1016/j.ins.2014.02.070
Chen, W., et al.: A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151, 147–160 (2017). https://doi.org/10.1016/j.catena.2016.11.032
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spelling Paola Patricia, Ariza-ColpasAyala-Mantilla, Cristian EduardoPiñeres-Melo, Marlon-AlbertoVillate-Daza, DiegoMorales-Ortega, Roberto CesaDe-la-Hoz Franco, EmiroSanchez-Moreno, HernandoButt Aziz, ShariqCollazos Morales, Carlos2021-10-26T19:45:50Z2021-10-26T19:45:50Z2021https://hdl.handle.net/11323/8806https://doi.org/10.1007/978-3-030-84340-3_19Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Maritime safety has become a relevant aspect in logistics processes using rivers. In Colombia, specifically in the Caribbean Region, there is the Magdalena River, a body of water that broadly borders the Colombian territory and is a tributary of various economic and public health activities. At its mouth, this river interacts with the sea directly, which generates a phenomenon called saline wedge, which is directly related to the sediments that must be continuously extracted and which threatens the proper functioning of the port from the city of Barranquilla, Colombia. Through this research, a network of sensors located in strategic places at the mouth of this river was generated, which allows predicting the behavior of the salt wedge. Using artificial neural networks, more specifically, the Multilayer Perceptron algorithm, it was possible to analyze the results of the implementation in light of the indicators or quality metrics, generating a highly reliable scenario that can be replicated in other sections of the river and in other aquifers.Paola Patricia, Ariza-ColpasAyala-Mantilla, Cristian EduardoPiñeres-Melo, Marlon-AlbertoVillate-Daza, DiegoMorales-Ortega, Roberto CesaDe-la-Hoz Franco, EmiroSanchez-Moreno, HernandoButt Aziz, ShariqCollazos Morales, Carlosapplication/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Computer Information Systems and Industrial Managementhttps://link.springer.com/chapter/10.1007/978-3-030-84340-3_19IOT systemsMachine learningSalt wedgeAquifersMagdalena river estuaryMultilayer PreceptronMultilayer Perceptron applied to the IOT systems for identification of saline wedge in the Magdalena estuary - ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionKellog, J.: Cenozoic tectonic history of the Sierra de Perijá, Venezuela-Colombia, and adjacent basins. Geol. Soc. Am. Mem. 162, 239–261 (1984)Restrepo, J.D., Kjerfve, B.: Magdalena river: interannual variability (1975–1995) and revised water discharge and sediment load estimates. J. Hydrol. 235, 137–149 (2000)Restrepo, J.D., Kjerfve, B.: The pacific and Caribbean rivers of Colombia: water discharge, sediment transport and dissolved loads. In: Lacerda, L., Santelli, R., Duursma, E., Abrao, J. (eds.) Environmental Geochemistry in Tropical and Subtropical Environments, pp. 169–187. Springer, Berlín (2004). https://doi.org/10.1007/978-3-662-07060-4_14Restrepo, J.D.: Applicability of LOICZ catchment-coast continuum in a major Caribbean basin: the Magdalena River, Colombia. Estuar. Coast. Shelf Sci. 77, 214–229 (2008)Restrepo, J.C., Otero, L., Lopez, S.: Clima de oleaje en el Pacifico sur de Colombia, delta del Río de Mira: Comparaciones Estadísticas y Aplicación a procesos Costeros. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales. 128(33), 339–357 (2009)Bhattacharya, B., Price, R.K., Solomatine, D.P.: Machine learning approach to modeling sediment transport. J. Hydraul. Eng. 133(4), 440–450 (2007)Fisher, L.H.: Sediment dynamics in the Magdalena river basin, Colombia: implications for understanding tropical river processes and hydropower development (2020)Alizamir, M., et al.: Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS ONE 15(4), e0231055 (2020)Li, Y., Wang, X., Zhao, Z., Han, S., Liu, Z.: Lagoon water quality monitoring based on digital image analysis and machine learning estimators. Water Res. 172, 115471 (2020)Alfonso, L., Tefferi, M.: Effects of uncertain control in transport of water in a river-wetland system of the Low Magdalena River, Colombia. In: Ocampo-Martinez, C., Negenborn, R.R. (eds.) Transport of water versus transport over water, pp. 131–144. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16133-4_8Ren, J., et al.: Multi-objective optimization of wave break forest design through machine learning. J. Hydroinf. 21(2), 295–307 (2019)Anfuso, G., Rangel-Buitrago, N., Arango, I.D.C.: Evolution of sandspits along the Caribbean coast of Colombia: natural and human influences. In: Randazzo, G., Jackson, D.W.T., Andrew, J., Cooper, G. (eds.) Sand and Gravel Spits, pp. 1–19. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13716-2_1Phillips, A.: Modelling riverine dissolved silica on different spatial and ttemporal scales using statistical and machine learning methods. Doctoral dissertation (2020)Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., Ghalhari, G.A.F.: Machine learning to estimate surface soil moisture from remote sensing data. Water 12(11), 3223 (2020)Björk, K.-M., Eirola, E., Miche, Y., Lendasse, A.: A new application of machine learning in health care, pp. 1–4 (2016). https://doi.org/10.1145/2910674.2935861Ariza Colpas, P., Vicario, E., De-La-Hoz-Franco, E., Pineres-Melo, M., Oviedo-Carrascal, A., Patara, F.: Unsupervised human activity recognition using the clustering approach: a review. Sensors 20(9), 2702 (2020)Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)Amiribesheli, M., Benmansour, A., Bouchachia, A.: A review of smart homes in healthcare. J. Ambient. Intell. Humaniz. Comput. 6(4), 495–517 (2015). https://doi.org/10.1007/s12652-015-0270-2Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf Technol. Biomed. 14(2), 274–283 (2010). https://doi.org/10.1109/TITB.2009.203731McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, no. 1, pp. 41–48, July 1998Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14(9), 755–763 (1998). https://academic.oup.com/bioinformatics/article-abstract/14/9/755/259550. Envejecimiento y salud (5 February 2018). https://www.who.int/es/news-room/fact-sheets/detail/envejecimiento-y-saludMurata, N., Yoshizawa, S., Amari, S.: Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Networks 5(6), 865–872 (1994). https://doi.org/10.1109/72.329683Du, W.S., Hu, B.Q.: Approximate distribution reducts in inconsistent interval-valued ordered decision tables. Inf. Sci. 271, 93–114 (2014). https://doi.org/10.1016/j.ins.2014.02.070Chen, W., et al.: A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151, 147–160 (2017). https://doi.org/10.1016/j.catena.2016.11.032PublicationORIGINALMultilayer Perceptron Applied to the IOT Systems for Identification of Saline Wedge in the Magdalena Estuary.pdfMultilayer Perceptron Applied to the IOT Systems for Identification of Saline Wedge in the Magdalena Estuary.pdfapplication/pdf90238https://repositorio.cuc.edu.co/bitstreams/89750cf1-e61d-4253-a717-b88448da2eb4/download8ea0a45c757505a5468188e4aa761eceMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/e4a6495c-12dd-4bc0-b68b-f31ffdde5713/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/eaff70a4-fd96-4288-8bc7-2c92959f103b/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMultilayer Perceptron Applied to the IOT Systems for Identification of Saline Wedge in the Magdalena Estuary.pdf.jpgMultilayer Perceptron Applied to the IOT 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