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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

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dc.citedby3
dc.contributor.authorZubaidi S.L.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorAl-Bugharbee H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorRidha H.M.en_US
dc.contributor.authorMo K.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57201458677en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid56433632700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214138178en_US
dc.contributor.authorid55915884700en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:46Z
dc.date.available2024-10-14T03:17:46Z
dc.date.issued2023
dc.description.abstractAccurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (�C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regressionen_US
dc.description.abstractmean absolute, relative and maximum error) and Taylor diagram. � 2023, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo184
dc.identifier.doi10.1007/s13201-023-01995-2
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85169164093
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85169164093&doi=10.1007%2fs13201-023-01995-2&partnerID=40&md5=cb4ff3f23cc1b7b2840388f0eb89a759
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34048
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.subjectAdaptive guided differential evolution algorithm
dc.subjectEleven learning algorithms
dc.subjectHybrid model
dc.subjectMonthly water demand
dc.subjectAustralia
dc.subjectMelbourne
dc.subjectVictoria [Australia]
dc.subjectDiscrete wavelet transforms
dc.subjectEvaporation
dc.subjectEvapotranspiration
dc.subjectEvolutionary algorithms
dc.subjectMean square error
dc.subjectMeteorology
dc.subjectOptimization
dc.subjectPrincipal component analysis
dc.subjectWater supply
dc.subjectWeather forecasting
dc.subjectAdaptive guided differential evolution algorithm
dc.subjectAustralia
dc.subjectDemand prediction
dc.subjectDifferential evolution algorithms
dc.subjectEleven learning algorithm
dc.subjectExtreme weather conditions
dc.subjectHybrid model
dc.subjectMelbourne
dc.subjectMonthly water demand
dc.subjectWater demand
dc.subjectaccuracy assessment
dc.subjectadaptive management
dc.subjectalgorithm
dc.subjectartificial neural network
dc.subjectclimate conditions
dc.subjectdatabase
dc.subjectextreme event
dc.subjectprediction
dc.subjectwater demand
dc.subjectwater use
dc.subjectLearning algorithms
dc.titleDeveloping a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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