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Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia

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dc.citedby7
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T09:07:21Z
dc.date.available2023-05-29T09:07:21Z
dc.date.issued2021
dc.descriptionerror analysis; global climate; machine learning; precision; prediction; streamflow; time series analysis; Australiaen_US
dc.description.abstractSustainable management of water supplies faces a comprehensive challenge due to global climate change. Improving forecasts of streamflow based on erratic precipitation is a significant activity nowadays. In recent years, the techniques of data-driven have been widely used in the hydrological parameter's prediction especially streamflow. In the current research, a deep learning model namely Long Short-Term Memory (LSTM), and two conventional machine learning models namely, Random Forest (RF), and Tree Boost (TB) were used to predict the streamflow of the Kowmung river at Cedar Ford in Australia. Different scenarios proposed to determine the optimal combination of input predictor variables, and the input predictor variables were selected based on the auto-correlation function (ACF). Model output was evaluated using indices of the root mean square error (RMSE), and the Nash and Sutcliffe coefficient (NSE). The findings showed that the LSTM model outperformed RF and TB in predicting the streamflow with RMSE and NSE equal to 102.411, and 0.911 respectively. for the LSTM model. The proposed model could adopt by hydrologists to solve the problems associated with forecasting daily streamflow with high precision. This study may not be generalized because of the geographical condition and the nature of the data for each location. � 2021 WITPress. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18280/IJSDP.160310
dc.identifier.epage501
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85108591812
dc.identifier.spage497
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85108591812&doi=10.18280%2fIJSDP.160310&partnerID=40&md5=e75b344b1b751012f8ae48995fb2da34
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26163
dc.identifier.volume16
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Sustainable Development and Planning
dc.titleApplication of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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