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

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

Convolution; 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 neur...

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Main Authors:	Chong K.L., Lai S.H., Yao Y., Ahmed A.N., Jaafar W.Z.W., El-Shafie A.
Other Authors:	57208482172
Format:	Article
Published:	Springer 2023
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id	my.uniten.dspace-25465
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spelling	my.uniten.dspace-254652023-05-29T16:09:45Z Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network Chong K.L. Lai S.H. Yao Y. Ahmed A.N. Jaafar W.Z.W. El-Shafie A. 57208482172 36102664300 57217068777 57214837520 55006925400 16068189400 Convolution; 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 Malaysia The 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. Final 2023-05-29T08:09:44Z 2023-05-29T08:09:44Z 2020 Article 10.1007/s11269-020-02554-z 2-s2.0-85086048126 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086048126&doi=10.1007%2fs11269-020-02554-z&partnerID=40&md5=5b044b0a39ed628cc4b4d0f335c466d4 https://irepository.uniten.edu.my/handle/123456789/25465 34 8 2371 2387 Springer Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Convolution; 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 Malaysia
author2	57208482172
author_facet	57208482172 Chong K.L. Lai S.H. Yao Y. Ahmed A.N. Jaafar W.Z.W. El-Shafie A.
format	Article
author	Chong K.L. Lai S.H. Yao Y. Ahmed A.N. Jaafar W.Z.W. El-Shafie A.
spellingShingle	Chong K.L. Lai S.H. Yao Y. Ahmed A.N. Jaafar W.Z.W. El-Shafie A. Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network
author_sort	Chong K.L.
title	Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network
title_short	Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network
title_full	Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network
title_fullStr	Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network
title_full_unstemmed	Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network
title_sort	performance enhancement model for rainfall forecasting utilizing integrated wavelet-convolutional neural network
publisher	Springer
publishDate	2023
_version_	1806427838219288576
score	13.222552

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

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