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A novel application of transformer neural network (TNN) for estimating pan evaporation rate

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dc.citedby19
dc.contributor.authorAbed M.en_US
dc.contributor.authorImteaz M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorid36612762700en_US
dc.contributor.authorid6506146119en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.date.accessioned2024-10-14T03:19:42Z
dc.date.available2024-10-14T03:19:42Z
dc.date.issued2023
dc.description.abstractFor decision-making in farming, the operation of dams and irrigation systems, as well as other fields of water resource management and hydrology, evaporation, as a key activity throughout the universal hydrological processes, entails efficient techniques for measuring its variation. The main challenge in creating accurate and dependable predictive models is the evaporation procedure's non-stationarity, nonlinearity, and stochastic characteristics. This work examines, for the first time, a transformer-based deep learning architecture for evaporation prediction in four different Malaysian regions. The effectiveness of the proposed deep learning (DL) model, signified as TNN, is evaluated against two competitive reference DL models, namely Convolutional Neural Network and Long Short-Term Memory, and with regards to various statistical indices using the monthly-scale dataset collected from four Malaysian meteorological stations in the 2000�2019 period. Using a variety of input variable combinations, the impact of every meteorological data on the Ep forecast is also examined. The performance assessment metrics demonstrate that compared to the other benchmark frameworks examined in this work, the developed TNN technique was more precise in modelling monthly water loss owing to evaporation. In terms of predictive effectiveness, the proposed TNN model, enhanced with the self-attention mechanism, outperforms the benchmark models, demonstrating its potential use in the forecasting of evaporation. Relating to application, the predictive model created for Ep projection offers a precise estimate of water loss due to evaporation and can thus be used in irrigation management, agriculture planning based on irrigation, and the decrease in fiscal and economic losses in farming and related industries where consistent supervision and estimation of water are considered necessary for viable living and economy. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo31
dc.identifier.doi10.1007/s13201-022-01834-w
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85145355729
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85145355729&doi=10.1007%2fs13201-022-01834-w&partnerID=40&md5=5cf086cf05d1329919e10d544719f9cc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34426
dc.identifier.volume13
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleApplied Water Science
dc.subjectConvolutional neural network
dc.subjectEvaporation
dc.subjectLong short-term memory
dc.subjectSelf-attention
dc.subjectTransformer neural network
dc.subjectMalaysia
dc.subjectBenchmarking
dc.subjectBrain
dc.subjectConvolution
dc.subjectConvolutional neural networks
dc.subjectCrops
dc.subjectDecision making
dc.subjectEvaporation
dc.subjectFarms
dc.subjectForecasting
dc.subjectIrrigation
dc.subjectStochastic models
dc.subjectStochastic systems
dc.subjectWater management
dc.subjectConvolutional neural network
dc.subjectLearning models
dc.subjectMalaysians
dc.subjectNeural-networks
dc.subjectNovel applications
dc.subjectPan evaporation
dc.subjectPredictive models
dc.subjectSelf-attention
dc.subjectTransformer neural network
dc.subjectWater loss
dc.subjectartificial neural network
dc.subjectdata set
dc.subjectevaporation
dc.subjectmachine learning
dc.subjectnumerical model
dc.subjectprediction
dc.subjectLong short-term memory
dc.titleA novel application of transformer neural network (TNN) for estimating pan evaporation rateen_US
dc.typeArticleen_US
dspace.entity.typePublication
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