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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

error analysis; global climate; machine learning; precision; prediction; streamflow; time series analysis; Australia

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Bibliographic Details
Main Authors: Latif S.D., Ahmed A.N.
Other Authors: 57216081524
Format: Article
Published: International Information and Engineering Technology Association 2023
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spelling my.uniten.dspace-261632023-05-29T17:07:21Z Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia Latif S.D. Ahmed A.N. 57216081524 57214837520 error analysis; global climate; machine learning; precision; prediction; streamflow; time series analysis; Australia Sustainable 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. Final 2023-05-29T09:07:21Z 2023-05-29T09:07:21Z 2021 Article 10.18280/IJSDP.160310 2-s2.0-85108591812 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108591812&doi=10.18280%2fIJSDP.160310&partnerID=40&md5=e75b344b1b751012f8ae48995fb2da34 https://irepository.uniten.edu.my/handle/123456789/26163 16 3 497 501 International Information and Engineering Technology Association 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 error analysis; global climate; machine learning; precision; prediction; streamflow; time series analysis; Australia
author2 57216081524
author_facet 57216081524
Latif S.D.
Ahmed A.N.
format Article
author Latif S.D.
Ahmed A.N.
spellingShingle Latif S.D.
Ahmed A.N.
Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
author_sort Latif S.D.
title Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
title_short Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
title_full Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
title_fullStr Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
title_full_unstemmed Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
title_sort application of deep learning method for daily streamflow time-series prediction: a case study of the kowmung river at cedar ford, australia
publisher International Information and Engineering Technology Association
publishDate 2023
_version_ 1806427633837146112
score 13.19449

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