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Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar Region (Algeria)

2026Artigo de investigação Acesso aberto
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datacite.subject.fosCiências Agrárias
dc.contributor.authorMeziani, Assia
dc.contributor.authorMega, Nabil
dc.contributor.authorMiloudi, Abdelmonen
dc.contributor.authorDuarte, A.C.
dc.date.accessioned2026-07-16T15:48:41Z
dc.date.available2026-07-16T15:48:41Z
dc.date.issued2026
dc.date.updated2026-07-09T16:33:17Z
dc.description.abstractAn accurate estimation of the radiation is a prerequisite for the net radiation balance, evapotranspiration modeling, and optimal scheduling of water extraction by solar-powered irrigation, especially in the water-scarce Saharan zone. In this study, we developed a predictive model to estimate daily terrestrial radiation at the surface of the Adrar region in Algeria. We used a training set of 25 years of data (2000–2025) from 10 stations (Adrar, Tamantit, Sidi Ahmed Timmi, Fenoughil, Zaouiet Kounta, Reggane, Gharmianou, Tittaf, Ikiss, Kassbet Lahrar) with only three features: soil temperature (0–7 cm), air temperature (2 m), and vapor pressure deficit. The robustness of the models was ensured by a time-based split. The random forest (RF), gradient boosting (GB), and extra trees (ET) tree-based models were evaluated on the generalization set. RF and ET exhibited the best performance (R = 0.92, rootean square error—RMSE = 31.85 W/m2, Nash–Sutcliffe efficiency—NSE = 0.84 for RF; R = 0.92, RMSE = 32.33 W/m2, NSE = 0.84 for ET), whereas GB exhibited poor performance (R = 0.90, RMSE = 36.67 W/m2, NSE = 0.79). Finally, we proposed a map for solar-powered irrigation optimization. In particular, we demonstrated that the southern part of the Adrar region (Reggane, Zaouiet Kounta) has high potential for solar-powered irrigation (more than 350 W/m2). This study contributes to hyper-arid agricultural regions in Algeria through water conservation and the utilization of renewable energy.eng
dc.description.versionN/A
dc.identifier.citationMEZIANI, A. [et al.] (2026) - Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar region (Algeria). Clean Energy Science and Technology. 4(3): 783. DOI: 10.18686/cest783
dc.identifier.doi10.18686/cest783en_US
dc.identifier.slugcv-prod-5066554
dc.identifier.urihttp://hdl.handle.net/10400.11/10945
dc.language.isoeng
dc.peerreviewedyes
dc.publisherUniverse Scientific Publishing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectTerrestrial radiation
dc.subjectMachine learning
dc.subjectRandom forest
dc.subjectSolar irrigation
dc.subjectAdrar region
dc.subjectHyper arid agriculture
dc.subjectRenewable energy
dc.titlePredictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar Region (Algeria)eng
dc.typeresearch articleen_US
dspace.entity.typePublication
oaire.citation.titleClean Energy Science and Technologyen_US
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCANATÁRIO DUARTE
person.givenNameANTÓNIO
person.identifier.ciencia-id0717-AB48-E1A3
person.identifier.orcid0000-0002-0319-378X
person.identifier.scopus-author-id54901177900
rcaap.cv.cienciaid0717-AB48-E1A3 | ANTÓNIO Canatário Duarte
rcaap.rightsrestrictedAccessen_US
relation.isAuthorOfPublicationff1ed167-3f68-4e2b-b092-aaa0eb28ae6b
relation.isAuthorOfPublication.latestForDiscoveryff1ed167-3f68-4e2b-b092-aaa0eb28ae6b

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