Gumbel function parameters estimation for annual maximum precipitation data using the Whale Optimization Algorithm (WOA)

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Abstract Hydrological variables frequency analysis is crucial for designing and operating hydraulic infrastructure. The Gumbel distribution is commonly used to model extreme events like floods. This study investigates the Whale Optimization Algorithm (WOA) use, a metaheuristic inspired by humpback whale hunting, to estimate the parameters of the Gumbel distribution for annual maximum precipitation data from a climatological station located at Hidalgo State, Mexico. The WOA was compared with moments method. Performance was evaluated using adjustment (EEA) standard error and root mean square error (RMSE). Results demonstrated comparable performance between these two methods. The WOA offers several advantages, including flexibility and adaptability to complex optimization problems. This study highlights the potential of the WOA as a valuable tool for parameter estimation in hydrological frequency analysis, improving the accuracy and reliability of designs for hydraulic structures under conditions of uncertainty and climate change.
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Gumbel function parameters estimation for annual maximum precipitation data using the Whale Optimization Algorithm (WOA) | 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 Gumbel function parameters estimation for annual maximum precipitation data using the Whale Optimization Algorithm (WOA) MARITZA L ARGANIS-JUÁREZ, Margarita E. Preciado-Jiménez, Alejandro Mendoza-Reséndiz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5822375/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hydrological variables frequency analysis is crucial for designing and operating hydraulic infrastructure. The Gumbel distribution is commonly used to model extreme events like floods. This study investigates the Whale Optimization Algorithm (WOA) use, a metaheuristic inspired by humpback whale hunting, to estimate the parameters of the Gumbel distribution for annual maximum precipitation data from a climatological station located at Hidalgo State, Mexico. The WOA was compared with moments method. Performance was evaluated using adjustment (EEA) standard error and root mean square error (RMSE). Results demonstrated comparable performance between these two methods. The WOA offers several advantages, including flexibility and adaptability to complex optimization problems. This study highlights the potential of the WOA as a valuable tool for parameter estimation in hydrological frequency analysis, improving the accuracy and reliability of designs for hydraulic structures under conditions of uncertainty and climate change. Frequency analysis Maximum Likelihood Method Bioinspired optimization algorithms rainfall Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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