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dc.contributor.authorRodero Gómez, Cristóbal
dc.contributor.otherEpifanio López, Irene
dc.contributor.otherUniversitat Jaume I. Departament de Matemàtiques
dc.date.accessioned2018-02-14T16:54:48Z
dc.date.available2018-02-14T16:54:48Z
dc.date.issued2017-11-23
dc.identifier.urihttp://hdl.handle.net/10234/172829
dc.descriptionTreball de Fi de Màster Universitari en Matemàtica Computacional (Pla de 2013). Codi: SIQ527. Curs 2016/2017ca_CA
dc.description.abstractIn ltration of salt water in water-treatment plants is a current challenge in Spanish coasts. This is a problem because when the sea water enters into the plant, it damages the lters and compromise the quality of the water ltration. In order to detect this water in ltration, the usual approach is to measure conductivity of the ows (ability to conduct electricity). The work subsequently described in this document is a project funded by a research internship of the Universitat Jaume I's C atedra FACSA de Innovaci on del Ciclo Integral del Agua. The goal of this project is to detect when a saltwater in ltration has occurred and to detect the most relevant variables which are related with the rising of water conductivity. The approach chosen to deal with this problem has been the technique of Random Forests, a family of algorithm based on decision trees. The reasons of this elections are mainly the exibility with respect missing data that Random Forests allow; the \black box"-like behaviour that does not need an a priori knowledge of the data structure; and the ability to explore the variables' importance through several measures. In this project the mentioned methodology will be explained in detail, as well as the results obtained and the conclusions that follows them.ca_CA
dc.format.extent51 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherUniversitat Jaume Ica_CA
dc.rightsAtribución-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectMàster Universitari en Matemàtica Computacionalca_CA
dc.subjectMáster Universitario en Matemática Computacionalca_CA
dc.subjectMaster's Degree in Computational Mathematicsca_CA
dc.subjectRandom Forestca_CA
dc.subjectVariable Importance Measureca_CA
dc.subjectSea Water Infiltrationca_CA
dc.subjectWater Conductivity Predictionca_CA
dc.titleAnalysis of sea water infiltration in a sewage treatment plant using Random Forests and variable importance measuresca_CA
dc.typeinfo:eu-repo/semantics/masterThesisca_CA
dc.educationLevelEstudios de Postgradoca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA


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Atribución-CompartirIgual 4.0 Internacional
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