Developing a novel parameter-free optimization framework for flood routing

Abstract The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for para...

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Autores principales:	Omid Bozorg-Haddad, Parisa Sarzaeim, Hugo A. Loáiciga
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0132c13c82e74df88805fcce879284c3
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spelling	oai:doaj.org-article:0132c13c82e74df88805fcce879284c32021-12-02T16:27:50ZDeveloping a novel parameter-free optimization framework for flood routing10.1038/s41598-021-95721-02045-2322https://doaj.org/article/0132c13c82e74df88805fcce879284c32021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95721-0https://doaj.org/toc/2045-2322Abstract The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous. Among the EMOAs teaching–learning-based optimization (TLBO) is a relatively new, parameter-free, and efficient metaheuristic optimization algorithm, inspired by the teacher-student interactions in a classroom to upgrade the overall knowledge of a topic through a teaching–learning procedure. The novelty of this study originates from (1) coupling TLBO and the nonlinear Muskingum routing model to estimate the Muskingum parameters by outflow predictability enhancement, and (2) evaluating a parameter-free algorithm’s functionality and accuracy involving complex Muskingum model’s parameter determination. TLBO, unlike previous EMOAs linked to the Muskingum model, is free of algorithmic parameters which makes it ideal for prediction without optimizing EMOAs parameters. The hypothesis herein entertained is that TLBO is effective in estimating the nonlinear Muskingum parameters efficiently and accurately. This hypothesis is evaluated with two popular benchmark examples, the Wilson and Wye River case studies. The results show the excellent performance of the “TLBO-Muskingum” for estimating accurately the Muskingum parameters based on the Nash–Sutcliffe Efficiency (NSE) to evaluate the TLBO’s predictive skill using benchmark problems. The NSE index is calculated 0.99 and 0.94 for the Wilson and Wye River benchmarks, respectively.Omid Bozorg-HaddadParisa SarzaeimHugo A. LoáicigaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Omid Bozorg-Haddad Parisa Sarzaeim Hugo A. Loáiciga Developing a novel parameter-free optimization framework for flood routing
description	Abstract The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous. Among the EMOAs teaching–learning-based optimization (TLBO) is a relatively new, parameter-free, and efficient metaheuristic optimization algorithm, inspired by the teacher-student interactions in a classroom to upgrade the overall knowledge of a topic through a teaching–learning procedure. The novelty of this study originates from (1) coupling TLBO and the nonlinear Muskingum routing model to estimate the Muskingum parameters by outflow predictability enhancement, and (2) evaluating a parameter-free algorithm’s functionality and accuracy involving complex Muskingum model’s parameter determination. TLBO, unlike previous EMOAs linked to the Muskingum model, is free of algorithmic parameters which makes it ideal for prediction without optimizing EMOAs parameters. The hypothesis herein entertained is that TLBO is effective in estimating the nonlinear Muskingum parameters efficiently and accurately. This hypothesis is evaluated with two popular benchmark examples, the Wilson and Wye River case studies. The results show the excellent performance of the “TLBO-Muskingum” for estimating accurately the Muskingum parameters based on the Nash–Sutcliffe Efficiency (NSE) to evaluate the TLBO’s predictive skill using benchmark problems. The NSE index is calculated 0.99 and 0.94 for the Wilson and Wye River benchmarks, respectively.
format	article
author	Omid Bozorg-Haddad Parisa Sarzaeim Hugo A. Loáiciga
author_facet	Omid Bozorg-Haddad Parisa Sarzaeim Hugo A. Loáiciga
author_sort	Omid Bozorg-Haddad
title	Developing a novel parameter-free optimization framework for flood routing
title_short	Developing a novel parameter-free optimization framework for flood routing
title_full	Developing a novel parameter-free optimization framework for flood routing
title_fullStr	Developing a novel parameter-free optimization framework for flood routing
title_full_unstemmed	Developing a novel parameter-free optimization framework for flood routing
title_sort	developing a novel parameter-free optimization framework for flood routing
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0132c13c82e74df88805fcce879284c3
work_keys_str_mv	AT omidbozorghaddad developinganovelparameterfreeoptimizationframeworkforfloodrouting AT parisasarzaeim developinganovelparameterfreeoptimizationframeworkforfloodrouting AT hugoaloaiciga developinganovelparameterfreeoptimizationframeworkforfloodrouting
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Developing a novel parameter-free optimization framework for flood routing

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