Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices f...

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Autores principales:	Antonio Agresta, Marco Baioletti, Chiara Biscarini, Fabio Caraffini, Alfredo Milani, Valentino Santucci
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	meta-heuristics river flow analysis manning’s coefficient Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/b13aac08f36b4ff5b6a2ca145ec31fd0
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id	oai:doaj.org-article:b13aac08f36b4ff5b6a2ca145ec31fd0
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spelling	oai:doaj.org-article:b13aac08f36b4ff5b6a2ca145ec31fd02021-11-25T16:31:55ZUsing Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis10.3390/app1122105752076-3417https://doaj.org/article/b13aac08f36b4ff5b6a2ca145ec31fd02021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10575https://doaj.org/toc/2076-3417Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s <i>n</i> coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘<i>n</i>’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.Antonio AgrestaMarco BaiolettiChiara BiscariniFabio CaraffiniAlfredo MilaniValentino SantucciMDPI AGarticlemeta-heuristicsriver flow analysismanning’s coefficientTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10575, p 10575 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	meta-heuristics river flow analysis manning’s coefficient Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	meta-heuristics river flow analysis manning’s coefficient Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Antonio Agresta Marco Baioletti Chiara Biscarini Fabio Caraffini Alfredo Milani Valentino Santucci Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
description	Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s <i>n</i> coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘<i>n</i>’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.
format	article
author	Antonio Agresta Marco Baioletti Chiara Biscarini Fabio Caraffini Alfredo Milani Valentino Santucci
author_facet	Antonio Agresta Marco Baioletti Chiara Biscarini Fabio Caraffini Alfredo Milani Valentino Santucci
author_sort	Antonio Agresta
title	Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
title_short	Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
title_full	Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
title_fullStr	Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
title_full_unstemmed	Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
title_sort	using optimisation meta-heuristics for the roughness estimation problem in river flow analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b13aac08f36b4ff5b6a2ca145ec31fd0
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Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

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