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A comprehensive review of artificial intelligence-based methods for predicting pan evaporation rate

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dc.citedby5
dc.contributor.authorAbed M.en_US
dc.contributor.authorImteaz M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid36612762700en_US
dc.contributor.authorid6506146119en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2024-10-14T03:17:45Z
dc.date.available2024-10-14T03:17:45Z
dc.date.issued2023
dc.description.abstractThis comprehensive study reviews the latest and most popular artificial intelligence (AI) techniques utilised for estimating pan evaporation (Ep), an essential parameter for water resource management and irrigation planning. Through an extensive evaluation of 76 papers published between 2006 and 2022, this study analyses the input data categories, time steps, properties, and capabilities of different AI models used for estimating Ep across various regions. The reviewed papers offer partial and comprehensive observations, providing valuable insights for researchers looking to model Ep in similar studies. Furthermore, this study proposes innovative theories and approaches to enhance the efficacy of Ep modelling in the relevant analysis domain. While hybrid AI techniques have gained popularity due to their perceived superiority over standalone deep learning and machine learning approaches, they often pose significant operational and computational challenges for Ep forecasting. As such, the study strongly recommends the use of transformer neural networks for Ep estimation, given their unique architecture and promising performance across various fields. Overall, this study presents a comprehensive and up-to-date overview of the latest AI-based techniques for estimating Ep and highlights the most promising approaches for future research. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s10462-023-10592-3
dc.identifier.epage2892
dc.identifier.scopus2-s2.0-85171427839
dc.identifier.spage2861
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85171427839&doi=10.1007%2fs10462-023-10592-3&partnerID=40&md5=89e560287520b6a1bde0df34ab9b3ff8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34041
dc.identifier.volume56
dc.pagecount31
dc.publisherSpringer Natureen_US
dc.sourceScopus
dc.sourcetitleArtificial Intelligence Review
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectPan evaporation
dc.subjectTransformer neural network
dc.subjectComputation theory
dc.subjectDeep learning
dc.subjectLearning systems
dc.subjectWater management
dc.subjectArtificial intelligence techniques
dc.subjectDeep learning
dc.subjectEvaporation rate
dc.subjectIrrigation planning
dc.subjectMachine-learning
dc.subjectManagement planning
dc.subjectNeural-networks
dc.subjectPan evaporation
dc.subjectTransformer neural network
dc.subjectWater resources management
dc.subjectEvaporation
dc.titleA comprehensive review of artificial intelligence-based methods for predicting pan evaporation rateen_US
dc.typeArticleen_US
dspace.entity.typePublication
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