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A study on the prediction of evapotranspiration using freely available meteorological data

2022Conference object Restricted access
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dc.contributor.authorJ. Vaz, Pedro
dc.contributor.authorSchütz, Gabriela
dc.contributor.authorGuerrero, Carlos
dc.contributor.authorCardoso, Pedro
dc.date.accessioned2023-03-31T10:29:03Z
dc.date.available2023-03-31T10:29:03Z
dc.date.issued2022
dc.description.abstractDue to climate change, the hydrological drought is assuming a structural character with a tendency to worsen in many countries. The frequency and intensity of droughts is predicted to increase, particularly in the Mediterranean region and in Southern Africa. Since a fraction of the fresh water that is consumed is used to irrigate urban fabric green spaces, which are typically made up of gardens, lanes and roundabouts, it is urgent to implement water waste prevention policies. Evapotranspiration (ETO) is a measurement that can be used to estimate the amount of water being taken up or used by plants, allowing a better management of the watering volumes but, the exact computation of the evapotranspiration volume is not possible without using complex and expensive sensor systems. In this study, several machine learning models were developed to estimate reference evapotranspiration and solar radiation from a reducedfeature dataset, such has temperature, humidity, and wind. Two main approaches were taken: (i) directly estimate ETO, or (ii) previously estimate solar radiation and then inject it into a function or method that computes ETO. For the later case, two variants were implemented, namely the use of the estimated solar radiation as (ii.1) a feature of the machine learning regressors and (ii.2) the use of FAO-56PM method to compute ETO, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station located in Vale do Lobo, south Portugal, the later approach achieved the best result with a coefficient of determination (R 2 ) of 0.975 over the test dataset. As a final notice, the reduced-set features were carefully selected so that they are compatible with online freely available weather forecast services.pt_PT
dc.description.sponsorshipALG-01-0247-FEDER-047030pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-031-08760-8_37pt_PT
dc.identifier.isbn978-3-031-08759-2
dc.identifier.urihttp://hdl.handle.net/10400.1/19369
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Verlagpt_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.subjectEvapotranspirationpt_PT
dc.subjectMachine learningpt_PT
dc.subjectEvapotranspirationpt_PT
dc.subjectMachine learningpt_PT
dc.subjectPublic gardenpt_PT
dc.subjectSmart irrigation.pt_PT
dc.titleA study on the prediction of evapotranspiration using freely available meteorological datapt_PT
dc.typeconference object
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.endPage450pt_PT
oaire.citation.startPage436pt_PT
oaire.citation.titleComputational Science – ICCS 2022: 22nd International Conferencept_PT
oaire.citation.volume13353pt_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.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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