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Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall

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dc.citedby15
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSheikh Khozani Z.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57185668800en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:32Z
dc.date.available2024-10-14T03:18:32Z
dc.date.issued2023
dc.description.abstractRainfall prediction is an important issue in water resource management. Predicting rainfall helps researchers to monitor droughts, surface water and floods. The current study introduces a new deep learning model named convolutional neural network (CONN)- support vector machine (SVM)- Gaussian regression process (GPR) to predict daily and monthly rainfall data in Terengganu River Basin, Malaysia. The CONN-SVM-GRP model can extract the most important features automatically. The main advantage of the new model is to reflect the uncertainty values in the modelling process. The lagged rainfall values were used as the input variables to the models. The proposed CONN-SVM-GRP model successfully decreased the Mean Absolute Error (MAE) of other models by 5.9%-23% at the daily scale and 20%-61% at the monthly scale. The CONN-SVM-GRP model also provided the lowest uncertainty among other models, making it a reliable tool for predicting data points and intervals. Hence, it can be concluded that CONN-SVM-GRP model contributes to the sustainable management of water resources, even when satellite data is unavailable, by using lagged values to predict rainfall. Additionally, the model extracts important features without using preprocessing methods, further improving its efficiency. Overall, the CONN-SVM-GRP model can help researchers predict rainfall, which is essential for monitoring water resources and mitigating the impacts of droughts, floods, and other natural disasters. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-023-03519-8
dc.identifier.epage3655
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85158137272
dc.identifier.spage3631
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85158137272&doi=10.1007%2fs11269-023-03519-8&partnerID=40&md5=2b72e29902a0d92320eeba078778c434
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34229
dc.identifier.volume37
dc.pagecount24
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.subjectDeep learning model
dc.subjectMachine Learning model
dc.subjectRainfall pattern
dc.subjectWater resource management
dc.subjectMalaysia
dc.subjectTerengganu
dc.subjectTerengganu Basin
dc.subjectWest Malaysia
dc.subjectConvolution
dc.subjectConvolutional neural networks
dc.subjectDeep learning
dc.subjectDisasters
dc.subjectDrought
dc.subjectFloods
dc.subjectForecasting
dc.subjectGaussian distribution
dc.subjectGaussian noise (electronic)
dc.subjectInformation management
dc.subjectLearning systems
dc.subjectRain
dc.subjectResource allocation
dc.subjectSurface waters
dc.subjectUncertainty analysis
dc.subjectWater management
dc.subjectConvolutional neural network
dc.subjectDaily rainfall
dc.subjectDeep learning model
dc.subjectLearning models
dc.subjectMachine learning models
dc.subjectMonthly rainfalls
dc.subjectNetwork support
dc.subjectRainfall patterns
dc.subjectSupport vectors machine
dc.subjectWater resources management
dc.subjectartificial neural network
dc.subjectclimate prediction
dc.subjectGaussian method
dc.subjectmachine learning
dc.subjectnatural disaster
dc.subjectprecipitation assessment
dc.subjectrainfall
dc.subjectsupport vector machine
dc.subjectwater resource
dc.subjectSupport vector machines
dc.titleConvolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfallen_US
dc.typeArticleen_US
dspace.entity.typePublication
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