AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

<p>Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water r...

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Autores principales:	Y. Guo, X. Yu, Y.-P. Xu, H. Chen, H. Gu, J. Xie
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/8b8c0a51ad8d4d2d8c59a04acce796c3
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spelling	oai:doaj.org-article:8b8c0a51ad8d4d2d8c59a04acce796c32021-11-18T09:13:17ZAI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment10.5194/hess-25-5951-20211027-56061607-7938https://doaj.org/article/8b8c0a51ad8d4d2d8c59a04acce796c32021-11-01T00:00:00Zhttps://hess.copernicus.org/articles/25/5951/2021/hess-25-5951-2021.pdfhttps://doaj.org/toc/1027-5606https://doaj.org/toc/1607-7938<p>Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.</p>Y. GuoX. YuY.-P. XuH. ChenH. GuJ. XieCopernicus PublicationsarticleTechnologyTEnvironmental technology. Sanitary engineeringTD1-1066Geography. Anthropology. RecreationGEnvironmental sciencesGE1-350ENHydrology and Earth System Sciences, Vol 25, Pp 5951-5979 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350
spellingShingle	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Y. Guo X. Yu Y.-P. Xu H. Chen H. Gu J. Xie AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
description	<p>Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.</p>
format	article
author	Y. Guo X. Yu Y.-P. Xu H. Chen H. Gu J. Xie
author_facet	Y. Guo X. Yu Y.-P. Xu H. Chen H. Gu J. Xie
author_sort	Y. Guo
title	AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
title_short	AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
title_full	AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
title_fullStr	AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
title_full_unstemmed	AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
title_sort	ai-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/8b8c0a51ad8d4d2d8c59a04acce796c3
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AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

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