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Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network

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dc.citedby26
dc.contributor.authorChong K.L.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorYao Y.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorJaafar W.Z.W.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid57217068777en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55006925400en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:09:44Z
dc.date.available2023-05-29T08:09:44Z
dc.date.issued2020
dc.descriptionConvolution; Discrete wavelet transforms; Rain; Signal reconstruction; Time series; Weather forecasting; Daily rainfall forecasting; Deep architectures; Monthly rainfalls; Performance enhancements; Performance indices; Rainfall forecasting; Statistical indices; Wavelet components; Convolutional neural networks; artificial neural network; data set; forecasting method; integrated approach; precipitation assessment; precipitation intensity; wavelet analysis; Langat Basin; Malaysia; West Malaysiaen_US
dc.description.abstractThe core objective of this study is to carry out rainfall forecasting over the Langat River Basin through the integration of wavelet transform (WT) and convolutional neural network (CNN). The proposed method involves using CNN for feature extraction to efficiently learn from the raw rainfall dataset. With the aid of deep architecture, a highly abstracted representation of the inputs time series with a high level of interpretation is formed at each subsequent CNN layer. The use of WT in forecasting the rainfall time series is by preprocessing the raw rainfall dataset into a set of decomposed wavelet components as inputs for the CNN model using discrete wavelet transform (DWT). The conditions for discretizing the raw input through DWT are discussed, along with the criteria to be used. Daily datasets, ranging from January 2002 to December 2017, were used. The results showed that the proposed model could satisfactorily capture patterns of the rainfall time series, for both monthly rainfalls forecasting or daily rainfall forecasting. Three performance indices were used to evaluate the model accuracy: RMSE, RSR, and MAE. These statistical indices have a range of value from 0 to a finite value that depends on the scale of the number used. In general, a lower value is better than a higher one. � 2020, Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-020-02554-z
dc.identifier.epage2387
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85086048126
dc.identifier.spage2371
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086048126&doi=10.1007%2fs11269-020-02554-z&partnerID=40&md5=5b044b0a39ed628cc4b4d0f335c466d4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25465
dc.identifier.volume34
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.titlePerformance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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