SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of...

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Autores:
Ariza Colpas, Paola Patricia
Ayala Mantilla, Cristian Eduardo
Shaheen, Qaisar
Piñeres-Melo, Marlon Alberto
Villate-Daza, Diego Andrés
Morales-Ortega, Roberto Cesar
De-La-Hoz-Franco, Emiro
Sanchez Moreno, Hernando
Aziz, Butt Shariq
AFzal, Mehtab
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:11323/8323
Acceso en línea:
https://hdl.handle.net/11323/8323
https://doi.org/10.3390/s21072374
https://repositorio.cuc.edu.co/
Palabra clave:
IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
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openAccess
License
CC0 1.0 Universal
id RCUC2_75432b8968a9b2ecfe7435bf7a3687cf
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8323
network_acronym_str RCUC2
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repository_id_str
dc.title.eng.fl_str_mv SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
title SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
spellingShingle SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
title_short SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
title_full SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
title_fullStr SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
title_full_unstemmed SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
title_sort SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary
dc.creator.fl_str_mv Ariza Colpas, Paola Patricia
Ayala Mantilla, Cristian Eduardo
Shaheen, Qaisar
Piñeres-Melo, Marlon Alberto
Villate-Daza, Diego Andrés
Morales-Ortega, Roberto Cesar
De-La-Hoz-Franco, Emiro
Sanchez Moreno, Hernando
Aziz, Butt Shariq
AFzal, Mehtab
dc.contributor.author.spa.fl_str_mv Ariza Colpas, Paola Patricia
Ayala Mantilla, Cristian Eduardo
Shaheen, Qaisar
Piñeres-Melo, Marlon Alberto
Villate-Daza, Diego Andrés
Morales-Ortega, Roberto Cesar
De-La-Hoz-Franco, Emiro
Sanchez Moreno, Hernando
Aziz, Butt Shariq
AFzal, Mehtab
dc.subject.eng.fl_str_mv IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
topic IOT systems
Machine learning
Salt wedge
Aquifers
Magdalena river estuary
description This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-01T15:21:30Z
dc.date.available.none.fl_str_mv 2021-06-01T15:21:30Z
dc.date.issued.none.fl_str_mv 2021-03-29
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 1424-3210
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identifier_str_mv 1424-3210
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Corporación Universidad de la Costa
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https://doi.org/10.3390/s21072374
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. De Guenni Leilys, B. El Diseño de Redes de Monitoreo: Teoria y Aplicaciones. In Jornadas Internacionales sobre Gestión del Riesgo de Inundaciones y Deslizamientos de Laderas; Universidad Simón Bolívar: Barranquilla, Brazil, 2007; pp. 5–6.
2. Steele, T.D. Water quality monitoring strategies. Hydrol. Sci. J. 1987, 32, 207–213. [CrossRef]
3. IDEAM. Estudio Ambiental del Magdalena–Cauca y Elementos Para su Ordenamiento Territorial; Cormagdalena: Bogotá, Colombia, 2001; p. 27.
4. Resolution 000272. Cargo Transportation along the Magdalena River after of the Two Governments of President Uribe. Available online: www.cormagdalena.gov.co (accessed on 12 March 2021).
5. Miller, C.A.; Kelley, A.L. Seasonality and biological forcing modify the diel frequency of nearshore pH extremes in a subarctic Alaskan estuary. Limnol. Oceanogr. 2021. [CrossRef]
6. Schrandt, M.N.; MacDonald, T.C.; Sherwood, E.T.; Beck, M.W. A multimetric nekton index for monitoring, managing, and communicating ecosystem health status in an urbanized Gulf of Mexico estuary. Ecol. Indic. 2021, 123, 107310. [CrossRef]
7. Lin, J.; Liu, X.; Lai, T.; He, B. Establishment and application of an evaluation system for the effectiveness of coastal wetland nature reserves management in Guangxi. Acta Ecol. Sin. 2020, 40, 1825–1833.
8. Patel, K.; Jain, R.; Patel, A.N.; Kalubarme, M.H. Shoreline change monitoring for coastal zone management using multi-temporal Landsat data in Mahi River estuary, Gujarat State. Appl. Geomat. 2021, 1–15. [CrossRef]
9. Vieira, K.S.; Crapez, M.A.C.; Lima, L.S.; Delgado, J.F.; Brito, E.B.C.C.; Fonseca, E.M.; Aguiar, V.M.C. Evaluation of bioavailability of trace metals through bioindicators in a urbanized estuarine system in southeast Brazil. Environ. Monit. Assess. 2021, 193, 1–16. [CrossRef] [PubMed]
10. Han, G.; Song, W.; Li, P.; Wang, X.; Wang, G.; Chu, X. Long-term ecological research support protection of coastal wetland ecosystems. Bull. Chin. Acad. Sci. 2020, 35, 218–228.
11. Chen, C.; Yuxi, S.; Jun, T. Research on Marine Disaster Prevention and Mitigation Information Platform System Based on Big Data. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: England, UK, 2021; Volume 632, p. 022082.
12. Hsieh, S.H.; Yuan, C.S.; Ie, I.R.; Yang, L.; Lin, H.J.; Hsueh, M.L. In-situ measurement of greenhouse gas emissions from a coastal estuarine wetland using a novel continuous monitoring technology: Comparison of indigenous and exotic plant species. J. Environ. Manag. 2021, 281, 111905. [CrossRef]
13. Dale, L.L.; Cronin, J.P.; Brink, V.L.; Tirpak, B.E.; Tirpak, J.M.; Pine, W.E., III. Identifying information gaps in predicting winter foraging habitat for juvenile Gulf Sturgeon Acipenser oxyrinchus desotoi. Trans. Am. Fish. Soc. 2020. [CrossRef]
14. Barthelemy, J.; Amirghasemi, M.; Arshad, B.; Fay, C.; Forehead, H.; Hutchison, N.; Perez, P. Problem-Driven and TechnologyEnabled Solutions for Safer Communities: The case of stormwater management in the Illawarra-Shoalhaven region (NSW, Australia). In Handbook of Smart Cities; Springer: Berlin/Heidelberg, Germany, 2020; pp. 1–28.
15. Bernal, G.; Poveda, G.; Roldán, P.; Andrade, C. Patrones de variabilidad de las temperaturas superficiales del mar en la costa Caribe colombiana. Rev. Acad. Colomb. Cienc. 2006, 30, 195–2008.
16. CIOH. Climatologia Barranquilla, Boletines Meteorologicos. Available online: www.cioh.org (accessed on 12 March 2021).
17. Vernette, G. Estandarización de los Criterios Sedimentológicos Para la Cartografía de la Plataforma Continental; Boletín Científico CIOH: Cartagena, Colombia, 1982; pp. 3–13.
18. Carson, M.A.; Taylor, C.H.; Grey, B.J. Sediment production in a small Appalachian watershed during spring runoff: The Eaton Basin, 1970–1972. Can. J. Earth Sci. 1973, 10, 1707–1734. [CrossRef]
19. Bernal, G.; Betancur, J. El Sistema Lagunar de la Ciénaga Grande de Santa Marta en el Contexto Deltaico del río Magdalena, Colombia. In Memorias IX Congreso Nacional de Ciencia y Tecnología del Mar, Medellín; CCO: Bogotá, Colombia, 1994.
20. Bernal, F.G. Caracterización Geomorfológica de la Llanura Deltaica del río Magdalena con Énfasis en el Sistema Lagunar de la Ciénaga Grande de Santa Marta, Colombia; Boletín de Investigaciones Marinas y Costeras: Santa Marta, Colombia, 1995; 52p.
21. González, A.M. Análisis de la evolución reciente de la morfología del cauce del Bajo Gallego en las proximidades de Zaragoza: Influencia de las actuaciones humanas en su entorno. Acta Geológica Hispánica 1991, 26, 23–33.
22. Lorin, J.; Hernández, C.; Rouault, A.; Bottagisio, J. Estudio Sedimentológico de la Plataforma Continental Entre Bocas de Ceniza y Santa Marta; MOPT: Puertos de Colombia, Barranquilla, 1973; 41p.
23. Martinez, M.; Molina, J.O.; Molina, L.H. Geomorfología y Aspectos Erosivos del Litoral Caribe Colombiano, Sector Bocas de Ceniza-Parque Tayrona; Ministry of Mines and Energy: Bogota, Colombia, 1992; 80p.
24. Ecólogos Ltda. Red Hidrográfica del Delta Exterior del Río Magdalena; Boletín de Investigaciones Marinas y Costeras: Santa Marta, Colombia, 1992.
25. Kaufmann, R.; Hevert, F. El régimen fluviométrico del río Magdalena y su importancia para la Ciénaga Grande de Santa Marta. Mitt. Inst. Colombo-Alemán Investig. Cient. 1973, 7, 121–137. [CrossRef]
26. Restrepo, J. Dinámica Sedimentaria en Deltas Micromareales–Estratificados de Alta Descarga: Delta del Rio Magdalena (Colombia– Mar Caribe). Ph.D. Thesis, Universidad del Norte, Barranquilla, Atlantico, Colombia, 2014; pp. 80–83.
27. Velasco, A.; Rodríguez, J.; Castillo, R.; Ortíz, I. Residues of organochlorine and organophosphorus pesticides in sugarcane crop soils and river water. J. Environ. Sci. Health Part B 2012, 47, 833–841. [CrossRef]
28. Sander, K. Specification of the basic body pattern in insect embryogenesis. In Advances in Insect Physiology; Academic Press: Cambridge, MA, USA, 1976; Volume 12, pp. 125–238.
29. Cuña e Intrusión Salina. El Heraldo. Available online: https://www.elheraldo.co/columnas-de-opinion/cuna-e-intrusionsalina-244510 (accessed on 12 March 2021).
30. 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 2020, 20, 2702. [CrossRef]
31. Sekeroglu, B.; Hasan, S.S.; Abdullah, S.M. Comparison of Machine Learning Algorithms for Classification Problems; Arai, K., Kapoor, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2020; Volume 944, pp. 491–499. [CrossRef]
32. Jordan, M.I.; Mitchell, T.M. Machine Learning: Trends, perspectives, and prospects. Science 2015, 349, 255–260. [CrossRef] [PubMed]
33. Ge, Z.; Song, Z.; Ding, S.X.; Huang, B. Data Mining and Analytics in the Process Industry: The Role of Machine Learning. IEEE Access 2017, 5, 20590–20616. [CrossRef]
34. Hota, S.; Jena, S.K.; Gupta, B.K.; Mishra, D. An Empirical Comparative Analysis of Nav Forecasting using Machine Learning Techniques; Smart Innovation, Systems and Technologies Series; Springer: Berlin/Heidelberg, Germany, 2021; Volume 153, pp. 565–572.
35. Pinter, G.; Felde, I.; Mosavi, A.; Ghamisi, P.; Gloaguen, R. COVID-19 pandemic prediction for Hungary; A hybrid machine learning approach. Mathematics 2020, 8, 890. [CrossRef]
36. Kim, G.-B.; Hwang, C.-I.; Shin, H.-J.; Choi, M.-R. Applicability of groundwater recharge rate estimation method based on artificial neural networks in unmeasured areas. J. Geol. Soc. Korea 2019, 55, 693–701. [CrossRef]
37. Von Schleinitz, J.; Worle, L.; Graf, M.; Schröder, A.; Trutschnig, W. Analysis of Race Car Drivers’ Pedal Interactions by means of Supervised Learning. IEEE Intell. Transp. Syst. Conf. ITSC 2019, 8917120, 4152–4157.
38. Laib, O.; Khadir, M.T.; Mihaylova, L. Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks. Energy 2019, 177, 530–542. [CrossRef]
39. Weytjens, H.; Lohmann, E.; Kleinsteuber, M. Cash Flow Prediction: MLP and LSTM Compared to ARIMA and Prophet. Electron. Commer. Res. 2019. [CrossRef]
40. Mohammed, B.; Hamdan, M.; Bassi, J.S.; Jamil, H.A.; Khan, S.; Elhigazi, A.; Marsono, M.N. Edge Computing Intelligence Using Robust Feature Selection for Network Traffic Classification in Internet-of-Things. IEEE Access 2020, 8, 224059–224070. [CrossRef]
41. Hamdan, M.; Mohammed, B.; Humayun, U.; Abdelaziz, A.; Khan, S.; Ali, M.A.; Marsono, M.N. Flow-aware elephant flow detection for software-defined networks. IEEE Access 2020, 8, 72585–72597. [CrossRef]
42. Dar, B.K.; Shah, M.A.; Islam, S.U.; Maple, C.; Mussadiq, S.; Khan, S. Delay-aware accident detection and response system using fog computing. IEEE Access 2019, 7, 70975–70985. [CrossRef]
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spelling Ariza Colpas, Paola PatriciaAyala Mantilla, Cristian EduardoShaheen, QaisarPiñeres-Melo, Marlon AlbertoVillate-Daza, Diego AndrésMorales-Ortega, Roberto CesarDe-La-Hoz-Franco, EmiroSanchez Moreno, HernandoAziz, Butt ShariqAFzal, Mehtab2021-06-01T15:21:30Z2021-06-01T15:21:30Z2021-03-291424-32101424-8220https://hdl.handle.net/11323/8323https://doi.org/10.3390/s21072374Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Ayala Mantilla, Cristian Eduardo-will be generated-orcid-0000-0002-1475-7827-600Shaheen, Qaisar-will be generated-orcid-0000-0002-1839-1412-600Piñeres-Melo, Marlon AlbertoVillate-Daza, Diego AndrésMorales-Ortega, Roberto CesarDe-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Sanchez Moreno, Hernando-will be generated-orcid-0000-0003-1435-1760-600Aziz, Butt ShariqAFzal, Mehtab-will be generated-orcid-0000-0002-9639-7350-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sensorshttps://www.mdpi.com/1424-8220/21/7/2374IOT systemsMachine learningSalt wedgeAquifersMagdalena river estuarySISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuaryArtículo 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/acceptedVersion1. De Guenni Leilys, B. El Diseño de Redes de Monitoreo: Teoria y Aplicaciones. In Jornadas Internacionales sobre Gestión del Riesgo de Inundaciones y Deslizamientos de Laderas; Universidad Simón Bolívar: Barranquilla, Brazil, 2007; pp. 5–6.2. Steele, T.D. Water quality monitoring strategies. Hydrol. Sci. J. 1987, 32, 207–213. [CrossRef]3. IDEAM. Estudio Ambiental del Magdalena–Cauca y Elementos Para su Ordenamiento Territorial; Cormagdalena: Bogotá, Colombia, 2001; p. 27.4. Resolution 000272. Cargo Transportation along the Magdalena River after of the Two Governments of President Uribe. Available online: www.cormagdalena.gov.co (accessed on 12 March 2021).5. Miller, C.A.; Kelley, A.L. Seasonality and biological forcing modify the diel frequency of nearshore pH extremes in a subarctic Alaskan estuary. Limnol. Oceanogr. 2021. [CrossRef]6. Schrandt, M.N.; MacDonald, T.C.; Sherwood, E.T.; Beck, M.W. A multimetric nekton index for monitoring, managing, and communicating ecosystem health status in an urbanized Gulf of Mexico estuary. Ecol. Indic. 2021, 123, 107310. [CrossRef]7. Lin, J.; Liu, X.; Lai, T.; He, B. Establishment and application of an evaluation system for the effectiveness of coastal wetland nature reserves management in Guangxi. Acta Ecol. Sin. 2020, 40, 1825–1833.8. Patel, K.; Jain, R.; Patel, A.N.; Kalubarme, M.H. Shoreline change monitoring for coastal zone management using multi-temporal Landsat data in Mahi River estuary, Gujarat State. Appl. Geomat. 2021, 1–15. [CrossRef]9. Vieira, K.S.; Crapez, M.A.C.; Lima, L.S.; Delgado, J.F.; Brito, E.B.C.C.; Fonseca, E.M.; Aguiar, V.M.C. Evaluation of bioavailability of trace metals through bioindicators in a urbanized estuarine system in southeast Brazil. Environ. Monit. Assess. 2021, 193, 1–16. [CrossRef] [PubMed]10. Han, G.; Song, W.; Li, P.; Wang, X.; Wang, G.; Chu, X. Long-term ecological research support protection of coastal wetland ecosystems. Bull. Chin. Acad. Sci. 2020, 35, 218–228.11. Chen, C.; Yuxi, S.; Jun, T. Research on Marine Disaster Prevention and Mitigation Information Platform System Based on Big Data. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: England, UK, 2021; Volume 632, p. 022082.12. Hsieh, S.H.; Yuan, C.S.; Ie, I.R.; Yang, L.; Lin, H.J.; Hsueh, M.L. In-situ measurement of greenhouse gas emissions from a coastal estuarine wetland using a novel continuous monitoring technology: Comparison of indigenous and exotic plant species. J. Environ. Manag. 2021, 281, 111905. [CrossRef]13. Dale, L.L.; Cronin, J.P.; Brink, V.L.; Tirpak, B.E.; Tirpak, J.M.; Pine, W.E., III. Identifying information gaps in predicting winter foraging habitat for juvenile Gulf Sturgeon Acipenser oxyrinchus desotoi. Trans. Am. Fish. Soc. 2020. [CrossRef]14. Barthelemy, J.; Amirghasemi, M.; Arshad, B.; Fay, C.; Forehead, H.; Hutchison, N.; Perez, P. Problem-Driven and TechnologyEnabled Solutions for Safer Communities: The case of stormwater management in the Illawarra-Shoalhaven region (NSW, Australia). In Handbook of Smart Cities; Springer: Berlin/Heidelberg, Germany, 2020; pp. 1–28.15. Bernal, G.; Poveda, G.; Roldán, P.; Andrade, C. Patrones de variabilidad de las temperaturas superficiales del mar en la costa Caribe colombiana. Rev. Acad. Colomb. Cienc. 2006, 30, 195–2008.16. CIOH. Climatologia Barranquilla, Boletines Meteorologicos. Available online: www.cioh.org (accessed on 12 March 2021).17. Vernette, G. Estandarización de los Criterios Sedimentológicos Para la Cartografía de la Plataforma Continental; Boletín Científico CIOH: Cartagena, Colombia, 1982; pp. 3–13.18. Carson, M.A.; Taylor, C.H.; Grey, B.J. Sediment production in a small Appalachian watershed during spring runoff: The Eaton Basin, 1970–1972. Can. J. Earth Sci. 1973, 10, 1707–1734. [CrossRef]19. Bernal, G.; Betancur, J. El Sistema Lagunar de la Ciénaga Grande de Santa Marta en el Contexto Deltaico del río Magdalena, Colombia. In Memorias IX Congreso Nacional de Ciencia y Tecnología del Mar, Medellín; CCO: Bogotá, Colombia, 1994.20. Bernal, F.G. Caracterización Geomorfológica de la Llanura Deltaica del río Magdalena con Énfasis en el Sistema Lagunar de la Ciénaga Grande de Santa Marta, Colombia; Boletín de Investigaciones Marinas y Costeras: Santa Marta, Colombia, 1995; 52p.21. González, A.M. Análisis de la evolución reciente de la morfología del cauce del Bajo Gallego en las proximidades de Zaragoza: Influencia de las actuaciones humanas en su entorno. Acta Geológica Hispánica 1991, 26, 23–33.22. Lorin, J.; Hernández, C.; Rouault, A.; Bottagisio, J. Estudio Sedimentológico de la Plataforma Continental Entre Bocas de Ceniza y Santa Marta; MOPT: Puertos de Colombia, Barranquilla, 1973; 41p.23. 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IEEE Access 2019, 7, 70975–70985. [CrossRef]PublicationORIGINALSisme, estuarine monitoring system based on iot and machine learning for the detection of salt wedge in aquifers. Case study of the magdalena river estuary.pdfSisme, estuarine monitoring system based on iot and machine learning for the detection of salt wedge in aquifers. 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