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Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

2021Journal article Open access
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dc.contributor.authorAlibabaei, Khadijeh
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorLima, Tânia M.
dc.date.accessioned2022-01-07T13:05:34Z
dc.date.available2022-01-07T13:05:34Z
dc.date.issued2021
dc.description.abstractIn recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.pt_PT
dc.description.sponsorshipProject Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/app11115029pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/11577
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCentre for Mechanical and Aerospace Science and Technologies
dc.subjectAgriculturept_PT
dc.subjectDeep learningpt_PT
dc.subjectLSTMpt_PT
dc.subjectSupport decision-making algorithmspt_PT
dc.subjectIrrigation managementpt_PT
dc.titleModeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Methodpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Mechanical and Aerospace Science and Technologies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00151%2F2020/PT
oaire.citation.issue11pt_PT
oaire.citation.startPage5029pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAlibabaei
person.familyNameGaspar
person.familyNameLima
person.givenNameKhadijeh
person.givenNamePedro Dinis
person.givenNameTânia
person.identifier1710267
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id771E-3B60-A936
person.identifier.orcid0000-0002-2319-8211
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-7540-3854
person.identifier.ridN-3016-2013
person.identifier.ridV-5052-2017
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id48661120000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationb69e2ba0-43af-4cf7-873e-090fd9fc6c94
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