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Impact of employing weather forecast data as input to the estimation of evapotranspiration by deep neural network models

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datacite.subject.sdg13:Ação Climática
datacite.subject.sdg06:Água Potável e Saneamento
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorMigueis Vaz Martins, Pedro Jorge
dc.contributor.authorSchütz, Gabriela
dc.contributor.authorGuerrero, Carlos
dc.contributor.authorCardoso, Pedro
dc.date.accessioned2026-06-03T14:29:00Z
dc.date.available2026-06-03T14:29:00Z
dc.date.issued2025
dc.description.abstractReference Evapotranspiration (𝐸𝑇!) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for 𝐸𝑇! computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute 𝐸𝑇! using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily 𝐸𝑇! estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several 𝐸𝑇! estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors’ previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct 𝐸𝑇! estimation by an Artificial Neural Network (ANN) model, and (ii) estimate SR by (another) ANN model, and then use that estimation for 𝐸𝑇! computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (𝑅") ranging between 0.893 and 0.667, when considering forecasts up to 15 days.eng
dc.identifier.doi10.1007/978-981-96-4345-5_5
dc.identifier.eissn2524-3438
dc.identifier.isbn9789819643448
dc.identifier.isbn9789819643455
dc.identifier.issn2524-342X
dc.identifier.urihttp://hdl.handle.net/10400.1/29088
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
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.relation.ispartofSpringer Proceedings in Earth and Environmental Sciences
dc.relation.ispartofNew Developments in Environmental and Energy Technologies
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learning
dc.subjectPublic garden
dc.subjectSmart irrigation
dc.titleImpact of employing weather forecast data as input to the estimation of evapotranspiration by deep neural network modelseng
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/50009/2020
oaire.awardNumberUIDB/00631/2020
oaire.awardNumberUIDP/00631/2020
oaire.awardNumberUIDB/05183/2020
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.conferenceDate2025
oaire.citation.titleNew Developments in Environmental and Energy Technologies
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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
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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
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