Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

Abstract Streamflow (Q flow ) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term M...

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Autores principales:	Sujan Ghimire, Zaher Mundher Yaseen, Aitazaz A. Farooque, Ravinesh C. Deo, Ji Zhang, Xiaohui Tao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f385ee43566f48ba899a6612e300ffb0
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spelling	oai:doaj.org-article:f385ee43566f48ba899a6612e300ffb02021-12-02T15:28:52ZStreamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks10.1038/s41598-021-96751-42045-2322https://doaj.org/article/f385ee43566f48ba899a6612e300ffb02021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96751-4https://doaj.org/toc/2045-2322Abstract Streamflow (Q flow ) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Q flow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Q flow time-series, while the LSTM networks use these features from CNN for Q flow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Q flow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Q flow , the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Q flow prediction error below the range of 0.05 m3 s−1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Q flow prediction.Sujan GhimireZaher Mundher YaseenAitazaz A. FarooqueRavinesh C. DeoJi ZhangXiaohui TaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sujan Ghimire Zaher Mundher Yaseen Aitazaz A. Farooque Ravinesh C. Deo Ji Zhang Xiaohui Tao Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
description	Abstract Streamflow (Q flow ) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Q flow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Q flow time-series, while the LSTM networks use these features from CNN for Q flow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Q flow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Q flow , the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Q flow prediction error below the range of 0.05 m3 s−1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Q flow prediction.
format	article
author	Sujan Ghimire Zaher Mundher Yaseen Aitazaz A. Farooque Ravinesh C. Deo Ji Zhang Xiaohui Tao
author_facet	Sujan Ghimire Zaher Mundher Yaseen Aitazaz A. Farooque Ravinesh C. Deo Ji Zhang Xiaohui Tao
author_sort	Sujan Ghimire
title	Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
title_short	Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
title_full	Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
title_fullStr	Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
title_full_unstemmed	Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
title_sort	streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f385ee43566f48ba899a6612e300ffb0
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Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks

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