Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/60173
DC FieldValueLanguage
dc.contributor.authorGuerra-Moreno, Ivánen_US
dc.contributor.authorNavarro-Mesa, Juan L.en_US
dc.contributor.authorRavelo-Garcia, Antonio G.en_US
dc.contributor.authorSuárez Araujo, Carmen Pazen_US
dc.contributor.authorRojas, Ien_US
dc.contributor.authorJoya, Gen_US
dc.contributor.authorCatala, Aen_US
dc.date.accessioned2020-01-15T11:29:27Z-
dc.date.available2020-01-15T11:29:27Z-
dc.date.issued2019en_US
dc.identifier.isbn978-3-030-20520-1en_US
dc.identifier.issn0302-9743en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/60173-
dc.description.abstractThe detection and quantification of rainfall is of paramount importance in many application contexts. The research work we present here is devoted to design a system to detect meteorological phenomena in situations of risk. Particularly, we extend the usage of systems designed for other specific purposes incorporating them weather observation as a new service. We investigate how machine learning techniques can be used to design rain detection and quantification algorithms that learn directly from data and become robust enough to perform detection under changing conditions over time. We show that Recurrent Neural Networks are well suited for rainfall quantification, and no precise knowledge of the underlying propagation model is necessary. We propose a recurrent neural architecture with two layers. A first layer acts as a detector-quantifier and it is trained using known precipitations from near rain gauges. The second layer plays the role of calibration that transforms previous levels into rainfall quantitation values. A very important aspect of our proposal is the feature extraction module, which allow a reliable detection and accurate quantification. We study several options for extracting the most discriminative features associated to rain and no rain events. We show that our system can detect and quantify rain-no rain events with promising results in terms of sensitivity (76,6%), specificity (97,0%) and accuracy (96,5%). The histograms of error and the accumulated rain rates show good performance, as well.en_US
dc.languageengen_US
dc.relationSistema de Vigilancia Meteorológica para el Seguimiento de Riesgos Medioambientalesen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceRojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11506. Springer, Chamen_US
dc.subject120304 Inteligencia artificialen_US
dc.subject25 Ciencias de la tierra y del espacioen_US
dc.subject.otherRecurrent neural networksen_US
dc.subject.otherPattern recognitionen_US
dc.subject.otherRain detectionen_US
dc.subject.otherRainfall quantificationen_US
dc.subject.otherReceived signal strengthen_US
dc.subject.otherMicrowave linksen_US
dc.titleOn the application of a recurrent neural network for rainfall quantification based on the received signal from microwave Linksen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConference_Paperes
dc.relation.conference15th International Work-Conference on Artificial Neural Networks (IWANN)
dc.relation.conference15th International Work-Conference on Artificial Neural Networks, IWANN 2019
dc.identifier.doi10.1007/978-3-030-20521-8_4
dc.identifier.scopus85067460186
dc.identifier.isi000490721600004-
dc.contributor.authorscopusid56006394700
dc.contributor.authorscopusid9634488300
dc.contributor.authorscopusid9634135600
dc.contributor.authorscopusid6603605708
dc.identifier.eissn1611-3349-
dc.description.lastpage51-
dc.description.firstpage39-
dc.relation.volume11506-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.contributor.daisngid8411268
dc.contributor.daisngid2630721
dc.contributor.daisngid1986395
dc.contributor.daisngid9879072
dc.identifier.eisbn978-3-030-20521-8-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Guerra-Moreno, I
dc.contributor.wosstandardWOS:Navarro-Mesa, JL
dc.contributor.wosstandardWOS:Ravelo-Garcia, AG
dc.contributor.wosstandardWOS:Suarez-Araujo, CP
dc.date.coverdate2019
dc.identifier.conferenceidevents121654
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.event.eventsstartdate12-06-2019-
crisitem.event.eventsstartdate12-06-2019-
crisitem.event.eventsenddate14-06-2019-
crisitem.event.eventsenddate14-06-2019-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptIDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptIDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptComputación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.orcid0000-0003-3860-3424-
crisitem.author.orcid0000-0002-8512-965X-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.fullNameGuerra Moreno, Ivan Daniel-
crisitem.author.fullNameNavarro Mesa, Juan Luis-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
crisitem.author.departamentoSeñales y Comunicaciones-
crisitem.author.departamentoSeñales y Comunicaciones-
crisitem.author.departamentoInformática y Sistemas-
Appears in Collections:Actas de congresos
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