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Hybrid neural network based models for evapotranspiration prediction over limited weather parameters

2023-01Journal article Open Access
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dc.contributor.authorVaz, Pedro J.
dc.contributor.authorSchutz, G.
dc.contributor.authorGuerrero, Carlos
dc.contributor.authorCardoso, Pedro
dc.date.accessioned2023-02-15T10:50:59Z
dc.date.available2023-02-15T10:50:59Z
dc.date.issued2023-01
dc.description.abstractEvapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration (ETo) estimation models, having as input a limited set of meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of ETo, SR estimation models are also developed. These ETo and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN, and GRU-ANN. Two main approaches were taken for ET(o )estimation: (i) directly use those algorithms to estimate ETo, and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts ETo. For the latter case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of the Penman-Monteith method (a.k.a. FAO-56PM method, adopted by the United Nations Food and Agriculture Organization) to compute ETo, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination (R-2) of 0.977. The developed model was then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar results to the ones for which the models were initially designed ((R2) > 0.95), proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.pt_PT
dc.description.sponsorshipALG-01-0247-FEDER-047030
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ACCESS.2022.3233301pt_PT
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.1/19087
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationMediterranean Institute for Agriculture, Environment and Development
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSolar radiationpt_PT
dc.subjectMeteorologypt_PT
dc.subjectCropspt_PT
dc.subjectComputational modelingpt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectPredictive modelspt_PT
dc.subjectIrrigationpt_PT
dc.subjectEvapotranspirationpt_PT
dc.subjectPublic gardenpt_PT
dc.subjectSmart irrigationpt_PT
dc.subjectSolar radiationpt_PT
dc.titleHybrid neural network based models for evapotranspiration prediction over limited weather parameterspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleMediterranean Institute for Agriculture, Environment and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05183%2F2020/PT
oaire.citation.endPage976pt_PT
oaire.citation.startPage963pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMigueis Vaz Martins
person.familyNameSchütz
person.familyNameGuerrero
person.familyNameCardoso
person.givenNamePedro Jorge
person.givenNameGabriela
person.givenNameCarlos
person.givenNamePedro
person.identifier.ciencia-id341B-DE9D-AFC7
person.identifier.ciencia-id821B-7ADF-B6DC
person.identifier.ciencia-id5F10-1C37-FE45
person.identifier.orcid0000-0002-8819-3243
person.identifier.orcid0000-0001-5081-3913
person.identifier.orcid0000-0001-9907-8235
person.identifier.orcid0000-0003-4803-7964
person.identifier.ridHKF-6445-2023
person.identifier.ridO-5305-2015
person.identifier.ridD-3485-2016
person.identifier.ridG-6405-2013
person.identifier.scopus-author-id35254562000
person.identifier.scopus-author-id24767767200
person.identifier.scopus-author-id35602693500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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