Developing a Novel Parameter-free Optimization Algorithm for Flood Routing

Developing a Novel Parameter-free Optimization Algorithm for Flood Routing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing a Novel Parameter-free Optimization Algorithm for Flood Routing Omid Bozorg-Haddad, Parisa Sarzaeim, Hugo A. Loáiciga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-228105/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Aug, 2021 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The Muskingum model is a popular hydrologic flood routing method; however, the accurate estimation of Muskingum model parameters is a critical task in the successful and precise implementation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation associated with various complex models including the nonlinear Muskingum model. Among EMOAs, teaching-learning-based optimization (TLBO) is a relatively new parameterless metaheuristic optimization algorithm, inspired by the relationship between teacher and students in a classroom to improve the overall knowledge of a topic in a class. This paper presents an application of TLBO to estimate Muskingum model parameters by minimizing the prediction error of outflow measurements. Several examples evaluate and confirm the successful performance of TLBO for the estimation of Muskingum-routing parameters precisely. The results show TLBO-Muskingum’s high accuracy for estimating accurately Muskingum’s parameters based on the Nash-Sutcliffe Efficiency (NSE) to evaluate the TLBO’s predictive skill with benchmark problems. Environmental Engineering Teaching-learning-based optimization (TLBO) algorithm Flood routing Parameter estimation Muskingum model Optimization Nash-sutcliffe efficiency Figures Figure 1 Figure 2 Figure 3 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Aug, 2021 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 28 Apr, 2021 Reviews received at journal 04 Apr, 2021 Reviewers agreed at journal 30 Mar, 2021 Reviews received at journal 30 Mar, 2021 Reviewers agreed at journal 30 Mar, 2021 Reviewers invited by journal 30 Mar, 2021 Editor assigned by journal 29 Mar, 2021 Editor invited by journal 19 Feb, 2021 Submission checks completed at journal 19 Feb, 2021 First submitted to journal 09 Feb, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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