Calibration of Manning’s roughness coefficients for shallow-water flows on complex bathymetries using optimization algorithms and surrogate neural network models
•The main features of a newly developed high-performance multi-GPU solver for the shallow-water equations are presented. This solver is used to build a database of highfidelity solutions.•A surrogate model based on an ensemble of neural networks is trained on the database.•Optimization algorithms ar...
Uložené v:
| Vydané v: | Computers & fluids Ročník 304; s. 106884 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
15.01.2026
|
| Predmet: | |
| ISSN: | 0045-7930 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •The main features of a newly developed high-performance multi-GPU solver for the shallow-water equations are presented. This solver is used to build a database of highfidelity solutions.•A surrogate model based on an ensemble of neural networks is trained on the database.•Optimization algorithms are analyzed to identify the optimal Manning friction coefficients using the surrogate model. Notably, no convergence issue is observed and accurate solutions are obtained.•Hybrid Particle Swarm Optimization (HPSO) algorithm revealed robust and fast convergence.•The versatility of this approach makes it applicable to various domains.
This paper presents an effective methodology for the automatic calibration of Manning’s roughness coefficients, which are crucial parameters for modeling shallow free-surface flows. Traditionally determined through empirical methods, these coefficients are subject to significant variability, making their determination challenging, especially in flow areas with complex bathymetry. The conventional trial-and-error approach, widely used to select these coefficients, is often tedious and time-consuming, particularly in applications constrained by time and data availability. The proposed methodology aims to determine the optimal values of Manning’s coefficients distributed over the flow domain while minimizing global discrepancies between simulations and field measurements. The calibration approach is formulated as an inverse optimization problem and addressed using metaheuristic optimization algorithms such as the Genetic Algorithm or Particle Swarm Optimization, combined with an ensemble model of deep neural networks. The database for training the neural networks is obtained using a newly developed finite volume-based shallow-water equations solver, parallelized on multiple GPUs, to generate large datasets of solutions for machine learning purposes. The performance of this approach is evaluated through various flow scenarios. Compared to conventional techniques, this methodology stands out for its simplicity, computational efficiency, and robustness. Additionally, Hybrid Particle Swarm Optimization (HPSO) proves to be particularly effective, notably for its speed. The developed codes are available at: https://github.com/ETS-GRANIT/CuteFlow. |
|---|---|
| AbstractList | •The main features of a newly developed high-performance multi-GPU solver for the shallow-water equations are presented. This solver is used to build a database of highfidelity solutions.•A surrogate model based on an ensemble of neural networks is trained on the database.•Optimization algorithms are analyzed to identify the optimal Manning friction coefficients using the surrogate model. Notably, no convergence issue is observed and accurate solutions are obtained.•Hybrid Particle Swarm Optimization (HPSO) algorithm revealed robust and fast convergence.•The versatility of this approach makes it applicable to various domains.
This paper presents an effective methodology for the automatic calibration of Manning’s roughness coefficients, which are crucial parameters for modeling shallow free-surface flows. Traditionally determined through empirical methods, these coefficients are subject to significant variability, making their determination challenging, especially in flow areas with complex bathymetry. The conventional trial-and-error approach, widely used to select these coefficients, is often tedious and time-consuming, particularly in applications constrained by time and data availability. The proposed methodology aims to determine the optimal values of Manning’s coefficients distributed over the flow domain while minimizing global discrepancies between simulations and field measurements. The calibration approach is formulated as an inverse optimization problem and addressed using metaheuristic optimization algorithms such as the Genetic Algorithm or Particle Swarm Optimization, combined with an ensemble model of deep neural networks. The database for training the neural networks is obtained using a newly developed finite volume-based shallow-water equations solver, parallelized on multiple GPUs, to generate large datasets of solutions for machine learning purposes. The performance of this approach is evaluated through various flow scenarios. Compared to conventional techniques, this methodology stands out for its simplicity, computational efficiency, and robustness. Additionally, Hybrid Particle Swarm Optimization (HPSO) proves to be particularly effective, notably for its speed. The developed codes are available at: https://github.com/ETS-GRANIT/CuteFlow. |
| ArticleNumber | 106884 |
| Author | Metcheka Kengne, Igor Gildas Delmas, Vincent Soulaïmani, Azzeddine |
| Author_xml | – sequence: 1 givenname: Igor Gildas surname: Metcheka Kengne fullname: Metcheka Kengne, Igor Gildas organization: Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Ouest, Montréal, H3C 1K3, QC, Canada – sequence: 2 givenname: Vincent orcidid: 0009-0007-4434-6627 surname: Delmas fullname: Delmas, Vincent organization: Institut de Mathématiques de Bordeaux (IMB), Université de Bordeaux, CNRS, Bordeaux INP, Talence, F33400, France – sequence: 3 givenname: Azzeddine orcidid: 0000-0003-3082-2155 surname: Soulaïmani fullname: Soulaïmani, Azzeddine email: azzeddine.soulaimani@etsmtl.ca organization: Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Ouest, Montréal, H3C 1K3, QC, Canada |
| BookMark | eNqFkDtuGzEQhlk4gF85Q3iBVbhc7koqDSEvwEYapyZmyaFEhUsKHG4Up8o1UvtmPokpKEibah7A_83gu2YXMUVk7F0rFq1oh_f7hUnTwYXZ24UUsq_bYbVSF-xKCNU3y3UnLtk10V7UuZPqij1vIPgxQ_Ep8uT4A8To4_bl9x_iOc3bXUQibhI6543HWIi7lDntIIR0bI5QMHNXW-IVcLoe8CcfoeyeJizZI_GZKpCnQ_GT_3U-BGGbsi-7iThEy2nOOW0rikecM4RayjHl73xKFgPdsjcOAuHbv_WGffv44XHzubn_-unL5u6-MV0rS2PA9bZXfQuD6JVrAcEOvRFuHBVitzJG9krZAY0cpAK1qoKsXMMoB7t2S9fdsOWZa3Iiyuj0IfsJ8pNuhT751Xv9z68--dVnvzV5d07Wb_GHx6zpJMug9RlN0Tb5_zJeAWgxlE8 |
| Cites_doi | 10.3390/w13131830 10.1016/j.envsoft.2006.12.003 10.2166/ws.2020.235 10.2166/hydro.2013.030 10.1016/S1001-6058(09)60051-2 10.1016/j.cpc.2021.108190 10.1137/S1064827595287997 10.1007/s12209-009-0078-2 10.1002/fld.801 10.1029/WR008i004p00956 10.1080/10618560410001710496 10.1016/j.advwatres.2014.11.009 10.1016/j.advwatres.2013.09.019 10.1016/j.jcp.2007.03.031 10.1016/j.jcp.2020.109854 10.1007/978-3-030-43651-3_42 10.1016/j.engappai.2022.105151 10.1137/S1064827503431090 10.1080/15715124.2017.1298605 10.1080/09715010.2017.1348263 10.1080/10618569908940814 10.1016/j.cma.2010.07.003 10.1137/070709359 10.1080/088395101750363966 10.1016/j.jcp.2022.111629 10.1007/978-981-19-1438-6_3 |
| ContentType | Journal Article |
| Copyright | 2025 The Author(s) |
| Copyright_xml | – notice: 2025 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.compfluid.2025.106884 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| ExternalDocumentID | 10_1016_j_compfluid_2025_106884 S0045793025003445 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JN AAEDT AAEDW AAFTH AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXUO AAYWO ABAOU ABJNI ABMAC ACDAQ ACGFS ACIWK ACLOT ACRLP ACVFH ADBBV ADCNI ADEZE ADGUI ADTZH AEBSH AECPX AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIGII AIGVJ AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP ARUGR AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFKBS EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JJJVA KOM MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SPD SST SSW SSZ T5K TN5 XPP ZMT ~G- ~HD 29F 6TJ 9DU AAQXK AAYXX ABDPE ABEFU ABFNM ABWVN ABXDB ACKIV ACNNM ACRPL ADIYS ADMUD ADNMO AFFNX AGQPQ AI. ASPBG AVWKF AZFZN CITATION EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ LG9 LY7 M41 R2- SBC SET T9H VH1 WUQ |
| ID | FETCH-LOGICAL-c312t-caf5d5451a6054f1aead65c0fbb4ee38cc2544d6ec2624a48025d29ab26d9f7f3 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001612532400002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0045-7930 |
| IngestDate | Thu Nov 27 00:42:05 EST 2025 Sat Nov 29 17:08:52 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Manning roughness coefficient CUDA Metaheuristic optimization algorithms Ensemble model Neural networks Multi-GPU Calibration Finite volumes Shallow-water equations |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c312t-caf5d5451a6054f1aead65c0fbb4ee38cc2544d6ec2624a48025d29ab26d9f7f3 |
| ORCID | 0000-0003-3082-2155 0009-0007-4434-6627 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.compfluid.2025.106884 |
| ParticipantIDs | crossref_primary_10_1016_j_compfluid_2025_106884 elsevier_sciencedirect_doi_10_1016_j_compfluid_2025_106884 |
| PublicationCentury | 2000 |
| PublicationDate | 2026-01-15 |
| PublicationDateYYYYMMDD | 2026-01-15 |
| PublicationDate_xml | – month: 01 year: 2026 text: 2026-01-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Computers & fluids |
| PublicationYear | 2026 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Delmas, Soulaïmani (bib0020) 2022; 471 Reshma, Reddy, Pratap, Agilan (bib0016) 2017; 24 Ziggaf, Boubekeur, kissami, Benkhaldoun, Mahi (bib0029) 2020 Joe, Kuo (bib0037) 2008; 30 Ding, Wang (bib0012) 2005; 19 Vidal, Moisan, Faure, Dartus (bib0003) 2007; 22 Agresta, Baioletti, Biscarini, Milani, Santucci (bib0018) 2021 Shahzadi, Soulaïmani (bib0041) 2021; 13 Tang, Xin, Dai, Xiao (bib0014) 2010; 22 Hagan, Demuth, Beale, De Jesús (bib0039) 2014 Jayakumar, Kumar (bib0017) 2017; 20 Ata, Pavan, Khelladi, Toro (bib0025) 2013; 62 Martínez-Aranda, Fernández-Pato, Echeverribar, Navas-Montilla, Morales-Hernández, Brufau (bib0028) 2022 Li, Geng, Mao (bib0007) 2020; 20 Soulaimani, Idrissi (bib0008) 1999; 12 Delmas (bib0019) 2020 Aslami, Rogers, Stansby, Bottacin-Busolin (bib0045) 2023; 1169 Gabriel, Fagg, Bosilca, Angskun, Dongarra, Squyres (bib0034) 2004 Goodfellow, Bengio, Courville (bib0038) 2016 Aureli, Dazzi, Maranzoni, Mignosa, Vacondio (bib0044) 2015; 76 Ata, Soulaimani (bib0026) 2005; 47 Suthar, Soulaimani (bib0032) 2018 Karypis, Kumar (bib0033) 1998; 20 Benkhaldoun, Elmahi, Seaïd (bib0030) 2010; 199 Poirier, Allmaras, McCarthy, Smith, Enomoto (bib0035) 1998; 39 Limerinos (bib0004) 1970 Yu, Tian, Zheng, Zhao (bib0013) 2009; 15 Yang, Wang, Tsung, Guo (bib0015) 2014; 16 Loukili, Soulaimani (bib0022) 2007; 8 Hameed Alwaeli L., Ali S.. Estimating of Manning’s roughness coefficient for hilla river through calibration using HEC-RAS model2013; 7:44–53. Becker, Yeh (bib0009) 1972; 8 Toro (bib0024) 2009 Jacquier, Abdedou, Delmas, Soulaïmani (bib0036) 2021; 424 Kumar (bib0042) 2015 Boulomytis, Zuffo, Dalfré Filho, Imteaz (bib0006) 2017; 15 Zokagoa (bib0023) 2011 Audusse, Bouchut, Bristeau, Klein, Perthame (bib0027) 2004; 25 Ganaie, Hu, Malik, Tanveer, Suganthan (bib0040) 2022; 115 Nguyen, Fenton (bib0011) 2004 Yao, Peng, Yu, Zhang, Luo (bib0002) 2022; 37 Delmas, Soulaïmani (bib0021) 2022; 271 Noelle, Xing, Shu (bib0031) 2007; 226 Lal (bib0010) 1995; 121 Ramesh, Datta, Bhallamudi, Narayana (bib0001) 2000; 126 Xu, Li, Wu (bib0043) 2001; 15 Delmas (10.1016/j.compfluid.2025.106884_bib0021) 2022; 271 Poirier (10.1016/j.compfluid.2025.106884_bib0035) 1998; 39 Ata (10.1016/j.compfluid.2025.106884_bib0026) 2005; 47 Yao (10.1016/j.compfluid.2025.106884_bib0002) 2022; 37 Becker (10.1016/j.compfluid.2025.106884_bib0009) 1972; 8 Xu (10.1016/j.compfluid.2025.106884_bib0043) 2001; 15 Agresta (10.1016/j.compfluid.2025.106884_bib0018) 2021 Limerinos (10.1016/j.compfluid.2025.106884_bib0004) 1970 Boulomytis (10.1016/j.compfluid.2025.106884_bib0006) 2017; 15 Karypis (10.1016/j.compfluid.2025.106884_bib0033) 1998; 20 Vidal (10.1016/j.compfluid.2025.106884_bib0003) 2007; 22 Lal (10.1016/j.compfluid.2025.106884_bib0010) 1995; 121 Reshma (10.1016/j.compfluid.2025.106884_bib0016) 2017; 24 Zokagoa (10.1016/j.compfluid.2025.106884_bib0023) 2011 Delmas (10.1016/j.compfluid.2025.106884_bib0020) 2022; 471 Goodfellow (10.1016/j.compfluid.2025.106884_bib0038) 2016 Suthar (10.1016/j.compfluid.2025.106884_bib0032) 2018 Yu (10.1016/j.compfluid.2025.106884_bib0013) 2009; 15 Benkhaldoun (10.1016/j.compfluid.2025.106884_bib0030) 2010; 199 Kumar (10.1016/j.compfluid.2025.106884_bib0042) 2015 Yang (10.1016/j.compfluid.2025.106884_bib0015) 2014; 16 Noelle (10.1016/j.compfluid.2025.106884_bib0031) 2007; 226 Li (10.1016/j.compfluid.2025.106884_bib0007) 2020; 20 Toro (10.1016/j.compfluid.2025.106884_bib0024) 2009 Ata (10.1016/j.compfluid.2025.106884_bib0025) 2013; 62 Loukili (10.1016/j.compfluid.2025.106884_bib0022) 2007; 8 Jacquier (10.1016/j.compfluid.2025.106884_bib0036) 2021; 424 Martínez-Aranda (10.1016/j.compfluid.2025.106884_bib0028) 2022 Delmas (10.1016/j.compfluid.2025.106884_bib0019) 2020 10.1016/j.compfluid.2025.106884_bib0005 Tang (10.1016/j.compfluid.2025.106884_bib0014) 2010; 22 Ramesh (10.1016/j.compfluid.2025.106884_bib0001) 2000; 126 Gabriel (10.1016/j.compfluid.2025.106884_bib0034) 2004 Aslami (10.1016/j.compfluid.2025.106884_bib0045) 2023; 1169 Nguyen (10.1016/j.compfluid.2025.106884_bib0011) 2004 Shahzadi (10.1016/j.compfluid.2025.106884_bib0041) 2021; 13 Joe (10.1016/j.compfluid.2025.106884_bib0037) 2008; 30 Audusse (10.1016/j.compfluid.2025.106884_bib0027) 2004; 25 Jayakumar (10.1016/j.compfluid.2025.106884_bib0017) 2017; 20 Ganaie (10.1016/j.compfluid.2025.106884_bib0040) 2022; 115 Soulaimani (10.1016/j.compfluid.2025.106884_bib0008) 1999; 12 Ding (10.1016/j.compfluid.2025.106884_bib0012) 2005; 19 Hagan (10.1016/j.compfluid.2025.106884_bib0039) 2014 Ziggaf (10.1016/j.compfluid.2025.106884_bib0029) 2020 Aureli (10.1016/j.compfluid.2025.106884_bib0044) 2015; 76 |
| References_xml | – volume: 16 start-page: 772 year: 2014 end-page: 783 ident: bib0015 article-title: Applying micro-genetic algorithm in the one-dimensional unsteady hydraulic model for parameter optimization publication-title: J Hydroinf – start-page: 97 year: 2004 end-page: 104 ident: bib0034 article-title: Open MPI: goals, concept, and design of a next generation MPI implementation publication-title: Proceedings, 11th European PVM/MPI Users’ group meeting – volume: 271 year: 2022 ident: bib0021 article-title: Multi-GPU implementation of a time-explicit finite volume solver using CUDA and a CUDA-aware version of openMPI with application to shallow water flows publication-title: Comput Phys Commun – volume: 39 start-page: 98 year: 1998 end-page: 3007 ident: bib0035 article-title: The CGNS system publication-title: AIAA Paper – volume: 15 start-page: 601 year: 2001 end-page: 631 ident: bib0043 article-title: A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move publication-title: Appl Artif Intell – volume: 15 start-page: 199 year: 2017 end-page: 206 ident: bib0006 article-title: Estimation and calibration of Manning’s roughness coefficients for ungauged watersheds on coastal floodplains publication-title: Int J River Basin Manage – volume: 20 year: 2017 ident: bib0017 article-title: Automated calibration of a two-dimensional overland flow model by estimating manning’s roughness coefficient using genetic algorithm publication-title: J Hydroinf – volume: 121 year: 1995 ident: bib0010 article-title: Calibration of riverbed roughness publication-title: J Hydraul Eng -asce - J HYDRAUL ENG-ASCE – year: 2020 ident: bib0019 publication-title: Implémentation multi-GPU d’un code en volumes finis pour le calcul de haute per formance des écoulements à sur face libre – reference: Hameed Alwaeli L., Ali S.. Estimating of Manning’s roughness coefficient for hilla river through calibration using HEC-RAS model2013; 7:44–53. – year: 2016 ident: bib0038 article-title: Deep learning – volume: 19 start-page: 3 year: 2005 end-page: 13 ident: bib0012 article-title: Identification of Manning’s roughness coefficients in channel network using adjoint analysis publication-title: Int J Comut Fluid Dyn – volume: 424 year: 2021 ident: bib0036 article-title: Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling publication-title: J Comput Phys – volume: 76 start-page: 29 year: 2015 end-page: 42 ident: bib0044 article-title: Experimental and numerical evaluation of the force due to the impact of a dam-break wave on a structure publication-title: Adv Water Resour – volume: 20 year: 2020 ident: bib0007 article-title: Calibration method for Manning’s roughness coefficient for a river flume model publication-title: Water Supply – volume: 37 year: 2022 ident: bib0002 article-title: Optimal inversion of Manning’s roughness in unsteady open flow simulations using adaptive parallel genetic algorithm publication-title: Water Resour Manage – volume: 8 start-page: 956 year: 1972 end-page: 965 ident: bib0009 article-title: Identification of parameters in unsteady open channel flows publication-title: Water Resour Res – year: 2009 ident: bib0024 article-title: Riemann solvers and numerical methods for fluid dynamics: a practical introduction – year: 1970 ident: bib0004 article-title: Determination of the manning coefficient for measured bed roughness in natural channels – volume: 30 start-page: 2635 year: 2008 end-page: 2654 ident: bib0037 article-title: Constructing sobol sequences with better two-dimensional projections publication-title: SIAM J Sci Comput – volume: 126 year: 2000 ident: bib0001 article-title: Optimal estimation of roughness in open-channel flows publication-title: J Hydraul Eng-asce - J HYDRAUL ENG-ASCE – volume: 471 year: 2022 ident: bib0020 article-title: Parallel high-order resolution of the shallow-water equations on real large-scale meshes with complex bathymetries publication-title: J Comput Phys – volume: 1169 year: 2023 ident: bib0045 article-title: Complex dam break simulation using the 2-D depth-averaged SPH flow model: a validation for tsunami application publication-title: IOP Conf Series: Earth and Environ Sci – volume: 115 year: 2022 ident: bib0040 article-title: Ensemble deep learning: a review publication-title: Eng Appl Artif Intell – volume: 22 start-page: 1628 year: 2007 end-page: 1640 ident: bib0003 article-title: River model calibration, from guidelines to operational support tools publication-title: Environmental Modelling and Software – year: 2011 ident: bib0023 publication-title: Modélisation numérique des écoulements à surface libre avec bancs couvrants-découvrants par les volumes finis et la décomposition orthogonale aux valeurs propres – year: 2014 ident: bib0039 article-title: Neural network design – volume: 226 start-page: 29 year: 2007 end-page: 58 ident: bib0031 article-title: High-order well-balanced finite volume WENO schemes for shallow water equation with moving water publication-title: J Comput Phys – volume: 20 start-page: 359 year: 1998 end-page: 392 ident: bib0033 article-title: A fast and high quality multilevel scheme for partitioning irregular graphs publication-title: SIAM J Sci Comput – volume: 25 start-page: 2050 year: 2004 end-page: 2065 ident: bib0027 article-title: A fast and stable well-balanced scheme with hydrostatic reconstruction for shallow water flows publication-title: SIAM J Sci Comput – volume: 62 start-page: 155 year: 2013 end-page: 172 ident: bib0025 article-title: A weighted average flux (WAF) scheme applied to shallow water equations for real-life applications publication-title: Adv Water Resour – volume: 8 start-page: 1 year: 2007 end-page: 14 ident: bib0022 article-title: Numerical tracking of shallow water waves by theunstructured finite volume WAF approximation publication-title: Int J Comput Methods Eng Mech – year: 2018 ident: bib0032 article-title: Parallelization of shallow water equations solver cuteflow publication-title: Tech. Rep. – volume: 199 start-page: 3324 year: 2010 end-page: 3335 ident: bib0030 article-title: A new finite volume method for flux-gradient and source-term balancing in shallow water equations publication-title: Comput Methods Appl Mech Eng – start-page: 795 year: 2021 end-page: 811 ident: bib0018 article-title: Evolutionary algorithms for roughness coefficient estimation in river flow analyses publication-title: Applications of evolutionary computation – volume: 12 start-page: 29 year: 1999 end-page: 48 ident: bib0008 article-title: Identification of the friction coefficient in shallow-water flows using optimal control theory publication-title: Int J Comut Fluid Dyn – start-page: 67 year: 2022 end-page: 137 ident: bib0028 article-title: Finite volume models and efficient simulation tools (EST) for shallow flows – volume: 15 start-page: 452 year: 2009 end-page: 456 ident: bib0013 article-title: Calibration of pipe roughness coefficient based on manning formula and genetic algorithm publication-title: Trans Tianjin Univ – start-page: 455 year: 2020 end-page: 465 ident: bib0029 article-title: The FVC scheme on unstructured meshes for the two-dimensional shallow water equations publication-title: Finite volumes for complex applications IX - Methods, theoretical aspects, examples – volume: 47 start-page: 139 year: 2005 end-page: 159 ident: bib0026 article-title: A stabilized SPH method for inviscid shallow water flows publication-title: Int J Numer Methods Fluids – year: 2015 ident: bib0042 article-title: Optimization algorithms and applications – year: 2004 ident: bib0011 article-title: Identification of roughness in open channels publication-title: Proc. of 6th international conference on hydro-science and engineering, Brisbane, Australia – volume: 24 start-page: 53 year: 2017 end-page: 60 ident: bib0016 article-title: Single- and two- step optimization of infiltration parameters and manning’s roughness coefficients for a watershed using a multi-objective genetic algorithm publication-title: ISH J Hydraul Eng – volume: 13 year: 2021 ident: bib0041 article-title: Deep neural network and polynomial chaos expansion-based surrogate models for sensitivity and uncertainty propagation: an application to a rockfill dam publication-title: Water – volume: 22 start-page: 246 year: 2010 end-page: 253 ident: bib0014 article-title: Parameter identification for modeling river network using a genetic algorithm publication-title: J Hydrodyn – volume: 13 issue: 13 year: 2021 ident: 10.1016/j.compfluid.2025.106884_bib0041 article-title: Deep neural network and polynomial chaos expansion-based surrogate models for sensitivity and uncertainty propagation: an application to a rockfill dam publication-title: Water doi: 10.3390/w13131830 – volume: 22 start-page: 1628 year: 2007 ident: 10.1016/j.compfluid.2025.106884_bib0003 article-title: River model calibration, from guidelines to operational support tools publication-title: Environmental Modelling and Software doi: 10.1016/j.envsoft.2006.12.003 – volume: 20 year: 2020 ident: 10.1016/j.compfluid.2025.106884_bib0007 article-title: Calibration method for Manning’s roughness coefficient for a river flume model publication-title: Water Supply doi: 10.2166/ws.2020.235 – year: 1970 ident: 10.1016/j.compfluid.2025.106884_bib0004 – year: 2004 ident: 10.1016/j.compfluid.2025.106884_bib0011 article-title: Identification of roughness in open channels – volume: 16 start-page: 772 year: 2014 ident: 10.1016/j.compfluid.2025.106884_bib0015 article-title: Applying micro-genetic algorithm in the one-dimensional unsteady hydraulic model for parameter optimization publication-title: J Hydroinf doi: 10.2166/hydro.2013.030 – volume: 121 year: 1995 ident: 10.1016/j.compfluid.2025.106884_bib0010 article-title: Calibration of riverbed roughness publication-title: J Hydraul Eng -asce - J HYDRAUL ENG-ASCE – start-page: 795 year: 2021 ident: 10.1016/j.compfluid.2025.106884_bib0018 article-title: Evolutionary algorithms for roughness coefficient estimation in river flow analyses – year: 2018 ident: 10.1016/j.compfluid.2025.106884_bib0032 article-title: Parallelization of shallow water equations solver cuteflow – year: 2014 ident: 10.1016/j.compfluid.2025.106884_bib0039 – volume: 22 start-page: 246 issue: 2 year: 2010 ident: 10.1016/j.compfluid.2025.106884_bib0014 article-title: Parameter identification for modeling river network using a genetic algorithm publication-title: J Hydrodyn doi: 10.1016/S1001-6058(09)60051-2 – volume: 37 year: 2022 ident: 10.1016/j.compfluid.2025.106884_bib0002 article-title: Optimal inversion of Manning’s roughness in unsteady open flow simulations using adaptive parallel genetic algorithm publication-title: Water Resour Manage – volume: 271 year: 2022 ident: 10.1016/j.compfluid.2025.106884_bib0021 article-title: Multi-GPU implementation of a time-explicit finite volume solver using CUDA and a CUDA-aware version of openMPI with application to shallow water flows publication-title: Comput Phys Commun doi: 10.1016/j.cpc.2021.108190 – volume: 126 year: 2000 ident: 10.1016/j.compfluid.2025.106884_bib0001 article-title: Optimal estimation of roughness in open-channel flows publication-title: J Hydraul Eng-asce - J HYDRAUL ENG-ASCE – volume: 20 start-page: 359 issue: 1 year: 1998 ident: 10.1016/j.compfluid.2025.106884_bib0033 article-title: A fast and high quality multilevel scheme for partitioning irregular graphs publication-title: SIAM J Sci Comput doi: 10.1137/S1064827595287997 – year: 2015 ident: 10.1016/j.compfluid.2025.106884_bib0042 – volume: 15 start-page: 452 year: 2009 ident: 10.1016/j.compfluid.2025.106884_bib0013 article-title: Calibration of pipe roughness coefficient based on manning formula and genetic algorithm publication-title: Trans Tianjin Univ doi: 10.1007/s12209-009-0078-2 – volume: 39 start-page: 98 year: 1998 ident: 10.1016/j.compfluid.2025.106884_bib0035 article-title: The CGNS system publication-title: AIAA Paper – volume: 47 start-page: 139 year: 2005 ident: 10.1016/j.compfluid.2025.106884_bib0026 article-title: A stabilized SPH method for inviscid shallow water flows publication-title: Int J Numer Methods Fluids doi: 10.1002/fld.801 – start-page: 97 year: 2004 ident: 10.1016/j.compfluid.2025.106884_bib0034 article-title: Open MPI: goals, concept, and design of a next generation MPI implementation – year: 2016 ident: 10.1016/j.compfluid.2025.106884_bib0038 – volume: 8 start-page: 956 issue: 4 year: 1972 ident: 10.1016/j.compfluid.2025.106884_bib0009 article-title: Identification of parameters in unsteady open channel flows publication-title: Water Resour Res doi: 10.1029/WR008i004p00956 – volume: 19 start-page: 3 year: 2005 ident: 10.1016/j.compfluid.2025.106884_bib0012 article-title: Identification of Manning’s roughness coefficients in channel network using adjoint analysis publication-title: Int J Comut Fluid Dyn doi: 10.1080/10618560410001710496 – volume: 76 start-page: 29 year: 2015 ident: 10.1016/j.compfluid.2025.106884_bib0044 article-title: Experimental and numerical evaluation of the force due to the impact of a dam-break wave on a structure publication-title: Adv Water Resour doi: 10.1016/j.advwatres.2014.11.009 – year: 2009 ident: 10.1016/j.compfluid.2025.106884_bib0024 – volume: 62 start-page: 155 year: 2013 ident: 10.1016/j.compfluid.2025.106884_bib0025 article-title: A weighted average flux (WAF) scheme applied to shallow water equations for real-life applications publication-title: Adv Water Resour doi: 10.1016/j.advwatres.2013.09.019 – volume: 226 start-page: 29 year: 2007 ident: 10.1016/j.compfluid.2025.106884_bib0031 article-title: High-order well-balanced finite volume WENO schemes for shallow water equation with moving water publication-title: J Comput Phys doi: 10.1016/j.jcp.2007.03.031 – volume: 424 year: 2021 ident: 10.1016/j.compfluid.2025.106884_bib0036 article-title: Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling publication-title: J Comput Phys doi: 10.1016/j.jcp.2020.109854 – year: 2011 ident: 10.1016/j.compfluid.2025.106884_bib0023 – start-page: 455 year: 2020 ident: 10.1016/j.compfluid.2025.106884_bib0029 article-title: The FVC scheme on unstructured meshes for the two-dimensional shallow water equations doi: 10.1007/978-3-030-43651-3_42 – volume: 115 year: 2022 ident: 10.1016/j.compfluid.2025.106884_bib0040 article-title: Ensemble deep learning: a review publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2022.105151 – volume: 25 start-page: 2050 issue: 6 year: 2004 ident: 10.1016/j.compfluid.2025.106884_bib0027 article-title: A fast and stable well-balanced scheme with hydrostatic reconstruction for shallow water flows publication-title: SIAM J Sci Comput doi: 10.1137/S1064827503431090 – volume: 15 start-page: 199 issue: 2 year: 2017 ident: 10.1016/j.compfluid.2025.106884_bib0006 article-title: Estimation and calibration of Manning’s roughness coefficients for ungauged watersheds on coastal floodplains publication-title: Int J River Basin Manage doi: 10.1080/15715124.2017.1298605 – year: 2020 ident: 10.1016/j.compfluid.2025.106884_bib0019 – volume: 24 start-page: 53 issue: 1 year: 2017 ident: 10.1016/j.compfluid.2025.106884_bib0016 article-title: Single- and two- step optimization of infiltration parameters and manning’s roughness coefficients for a watershed using a multi-objective genetic algorithm publication-title: ISH J Hydraul Eng doi: 10.1080/09715010.2017.1348263 – volume: 12 start-page: 29 year: 1999 ident: 10.1016/j.compfluid.2025.106884_bib0008 article-title: Identification of the friction coefficient in shallow-water flows using optimal control theory publication-title: Int J Comut Fluid Dyn doi: 10.1080/10618569908940814 – volume: 199 start-page: 3324 issue: 49 year: 2010 ident: 10.1016/j.compfluid.2025.106884_bib0030 article-title: A new finite volume method for flux-gradient and source-term balancing in shallow water equations publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2010.07.003 – volume: 30 start-page: 2635 issue: 5 year: 2008 ident: 10.1016/j.compfluid.2025.106884_bib0037 article-title: Constructing sobol sequences with better two-dimensional projections publication-title: SIAM J Sci Comput doi: 10.1137/070709359 – volume: 15 start-page: 601 issue: 6 year: 2001 ident: 10.1016/j.compfluid.2025.106884_bib0043 article-title: A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move publication-title: Appl Artif Intell doi: 10.1080/088395101750363966 – volume: 20 year: 2017 ident: 10.1016/j.compfluid.2025.106884_bib0017 article-title: Automated calibration of a two-dimensional overland flow model by estimating manning’s roughness coefficient using genetic algorithm publication-title: J Hydroinf – volume: 471 year: 2022 ident: 10.1016/j.compfluid.2025.106884_bib0020 article-title: Parallel high-order resolution of the shallow-water equations on real large-scale meshes with complex bathymetries publication-title: J Comput Phys doi: 10.1016/j.jcp.2022.111629 – volume: 8 start-page: 1 year: 2007 ident: 10.1016/j.compfluid.2025.106884_bib0022 article-title: Numerical tracking of shallow water waves by theunstructured finite volume WAF approximation publication-title: Int J Comput Methods Eng Mech – volume: 1169 year: 2023 ident: 10.1016/j.compfluid.2025.106884_bib0045 article-title: Complex dam break simulation using the 2-D depth-averaged SPH flow model: a validation for tsunami application publication-title: IOP Conf Series: Earth and Environ Sci – ident: 10.1016/j.compfluid.2025.106884_bib0005 – start-page: 67 year: 2022 ident: 10.1016/j.compfluid.2025.106884_bib0028 doi: 10.1007/978-981-19-1438-6_3 |
| SSID | ssj0004324 |
| Score | 2.4502056 |
| Snippet | •The main features of a newly developed high-performance multi-GPU solver for the shallow-water equations are presented. This solver is used to build a... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 106884 |
| SubjectTerms | Calibration CUDA Ensemble model Finite volumes Manning roughness coefficient Metaheuristic optimization algorithms Multi-GPU Neural networks Shallow-water equations |
| Title | Calibration of Manning’s roughness coefficients for shallow-water flows on complex bathymetries using optimization algorithms and surrogate neural network models |
| URI | https://dx.doi.org/10.1016/j.compfluid.2025.106884 |
| Volume | 304 |
| WOSCitedRecordID | wos001612532400002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0045-7930 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0004324 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ3LbtNAFIZHoWUBC8RVlEs1C3aWUezxlV0ELbRSK6QWlJ019swUF18iO25DV7wGa56AV-JJOHOx40IlQIhNZFnxOPH5cvL7-FwQegYSnoiIOLaIRWSDAqc29afcdkTMmYiEJxyqhk2Eh4fRfB6_nUy-9bUwZ0VYVdFqFS_-q6lhHxhbls7-hbmHRWEHbIPR4RXMDq9_ZHhZbZU2gxI80EOJ-qSGuLXUYB7l4bKaqw4SqspN5hu2crRKfW6fU9k7UcCmepqg8s75ykpBLn4q1Qyu1upUlKEGl1OaWk6LFid1ky8_lLrxc9s1TS3DdJbsmgksVDrnXI_face6uB8u0SoURdHlbBD7B4qsj1TWEJ3oAOwenMd6nReMDu96xYtSF6e9z6tslM5zVHcFVfkAu6UeYGXNLi44Y31GgYl5uCrmoas-dSCuL8ZZZz4p5-7J7pvmMY9x7kQPN_7lj0LHLE6lnRfqSz2H8_iwP4j0yLqfunAfydXl4iAZZZdE_xradEM_Bke6Odvbme-vi3GJq1t_m09zKanwytNdLYlGMuf4Nrpl7k_wTHN1B014dRfdHHWtvIe-jgjDtcCGsO-fv7R4YAuP2cLAFr7EFlZsYVjAsIXHbGHFFh6zhddsYWALD2xhzRY2bGHN1n30bnfn-OUb28z6sDPiuEs7o8JnoOYdCvfX0kGAhwv8bCrS1OOcRFkme-mxgGdu4HrUi-DyMTemqRuwWISCPEAbVV3xhwgHhKbEZ2HKMuq5PKUxJ8RhHjieyKOEbqFpf7mThW7pkvS5jqfJYKFEWijRFtpCL3qzJEaZasWZAE-_O_jRvxz8GN1Y_wCeoI1l0_Gn6Hp2tszbZtuw9wPR9sYI |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Calibration+of+Manning%E2%80%99s+roughness+coefficients+for+shallow-water+flows+on+complex+bathymetries+using+optimization+algorithms+and+surrogate+neural+network+models&rft.jtitle=Computers+%26+fluids&rft.au=Metcheka+Kengne%2C+Igor+Gildas&rft.au=Delmas%2C+Vincent&rft.au=Soula%C3%AFmani%2C+Azzeddine&rft.date=2026-01-15&rft.pub=Elsevier+Ltd&rft.issn=0045-7930&rft.volume=304&rft_id=info:doi/10.1016%2Fj.compfluid.2025.106884&rft.externalDocID=S0045793025003445 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0045-7930&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0045-7930&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0045-7930&client=summon |