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Spatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM models

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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.contributor.authorKhechekhouche, Abderahamane
dc.date.accessioned2026-07-16T15:41:25Z
dc.date.available2026-07-16T15:41:25Z
dc.date.issued2026
dc.date.updated2026-07-09T15:25:19Z
dc.descriptionThe meteorological data used in this study were retrieved from the Open-Meteo Historical Weather API (https://open-meteo.com ). The processed datasets and ET₀ calculations generated during the current study are available from the corresponding author upon reasonable request.
dc.description.abstractReference evapotranspiration (ET₀) is a key parameter for water management in semi-arid regions with variable climates. This study analyzed the spatiotemporal dynamics of annual ET₀ in the Khenchela region of north-eastern Algeria (2000–2024). ET₀ was computed using the FAO-56 Penman–Monteith (PM) method. Spatial patterns were mapped using Inverse Distance Weighting (IDW). Meteorological data from 16 stations were used to train three models: Multiple Linear Regression (MLR), Generalized Regression Neural Network (GRNN), and Long Short-Term Memory (LSTM) to predict ET₀. The regional mean annual ET₀ increased by 7.2% from 2010 to 2019 decadal average (1 490 mm/year) to the 2020-2024 period (1597 mm/year), contributing to a cumulative 25-year increase of 7% from 2000 to 2009 baseline with hotspots in Babar 2 reaching ~2194 mm/year. The Mann–Kendall test confirmed significant upward trends (p < 0.05) driven by rising temperatures and declining relative humidity. All models performed well (R² > 0.965, RMSE < 0.49 mm/day, RSR < 0.20), with LSTM showing superior accuracy (R² > 0.987, RMSE < 0.232 mm/day, NSE ≈ 0.991, WI > 0.909). The superior performance of LSTM is attributed to its inherent capability to capture temporal autocorrelation and long-term dependencies in climatic time-series data. These findings support adaptive irrigation and drought mitigation in semi-arid regions of northern Africa.eng
dc.description.versionN/A
dc.identifier.citationMEZIANI, Assia [et al.] (2026) - Spatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM models. Ambiente e Agua - An Interdisciplinary Journal of Applied Science. Vol. 21, e3152. DOI: 10.4136/ambi-agua.3152
dc.identifier.doi10.4136/ambi-agua.3152en_US
dc.identifier.issn1980-993Xen_US
dc.identifier.slugcv-prod-5067868
dc.identifier.urihttp://hdl.handle.net/10400.11/10944
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlgerian semi-arid region
dc.subjectClimate change
dc.subjectEvapotranspiration
dc.subjectFAO-56 Penman-Monteith
dc.subjectMachine learning
dc.subjectSemi-arid regions
dc.subjectWater resource management
dc.titleSpatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM modelseng
dc.typeresearch articleen_US
dspace.entity.typePublication
oaire.citation.endPage35
oaire.citation.startPage1
oaire.citation.titleAmbiente e Agua - An Interdisciplinary Journal of Applied Scienceen_US
oaire.citation.volume21en_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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