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

Antonio Agresta Orcid Logo, Marco Baioletti Orcid Logo, Chiara Biscarini Orcid Logo, Fabio Caraffini Orcid Logo, Alfredo Milani Orcid Logo, Valentino Santucci Orcid Logo

Applied Sciences, Volume: 11, Issue: 22, Start page: 10575

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.3390/app112210575

Abstract

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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Published in: Applied Sciences
ISSN: 2076-3417
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa60906
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In this context, being able to accurately estimate the roughness coefficient, also known as Manning&#x2019;s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of &#x2018;n&#x2019;. First, an objective function is designed for measuring the quality of &#x2018;candidate&#x2019; Manning&#x2019;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 &#x2018;Paglia&#x2019; and &#x2018;Aniene&#x2019;. 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spelling 2022-09-23T11:29:46.7353076 v2 60906 2022-08-28 Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 MACS 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 n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. 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. Journal Article Applied Sciences 11 22 10575 MDPI AG 2076-3417 meta-heuristics; river flow analysis; manning’s coefficient 10 11 2021 2021-11-10 10.3390/app112210575 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This research was partially funded by the following grants: (i) Università per Stranieri di Perugia—Progetto di ricerca Artificial Intelligence for Education, Social and Human Sciences; (ii) Università per Stranieri di Perugia—Finanziamento per Progetti di Ricerca di Ateneo — PRA 2021; (iii) Italian Ministry of the Environment Land and Sea (MATTM)—project GEST-RIVER Gestione ecosostenibile dei territori a rischio inondazione e valorizzazione economica delle risorse (Italian Law 5/1/2017, 4). 2022-09-23T11:29:46.7353076 2022-08-28T19:10:46.3254891 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Antonio Agresta 0000-0003-4037-7403 1 Marco Baioletti 0000-0001-5630-7173 2 Chiara Biscarini 0000-0003-2279-7244 3 Fabio Caraffini 0000-0001-9199-7368 4 Alfredo Milani 0000-0003-4534-1805 5 Valentino Santucci 0000-0003-1483-7998 6 60906__25198__a4211d1891f44ea5994f5d7d4bc6e3d0.pdf 60906_VoR.pdf 2022-09-23T11:28:26.0392361 Output 13791901 application/pdf Version of Record true © 2021 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
spellingShingle Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
Fabio Caraffini
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
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Antonio Agresta
Marco Baioletti
Chiara Biscarini
Fabio Caraffini
Alfredo Milani
Valentino Santucci
format Journal article
container_title Applied Sciences
container_volume 11
container_issue 22
container_start_page 10575
publishDate 2021
institution Swansea University
issn 2076-3417
doi_str_mv 10.3390/app112210575
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
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 n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. 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.
published_date 2021-11-10T20:14:29Z
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score 11.04748

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