Randomly Supported Variations of Deterministic Models and Their Application to One-Dimensional Shallow Water Flows-Reference-Cited by-同舟云学术

Randomly Supported Variations of Deterministic Models and Their Application to One-Dimensional Shallow Water Flows

Published:2024-09 Issue:5 Volume:150 Page:
ISSN:0733-9429
Container-title:Journal of Hydraulic Engineering
language:en
Short-container-title:J. Hydraul. Eng.

Author:

Birgin E. G.¹^ORCID,Correa M. R.²^ORCID,González-López V. A.³^ORCID,Martínez J. M.⁴^ORCID,Rodrigues D. S.⁵^ORCID

Affiliation:

1. Professor, Dept. of Computer Science, Institute of Mathematics and Statistics, Univ. of São Paulo, Rua do Matão, 1010, Cidade Universitária, São Paulo, SP 05508-090, Brazil (corresponding author). ORCID: .

2. Associate Professor, Dept. of Applied Mathematics, Institute of Mathematics, Statistics, and Scientific Computing, Univ. of Campinas, Campinas, SP 13083-859, Brazil. ORCID: .

3. Associate Professor, Dept. of Statistics, Institute of Mathematics, Statistics, and Scientific Computing, Univ. of Campinas, Campinas, SP 13083-859, Brazil. ORCID: .

4. Emeritus Professor, Dept. of Applied Mathematics, Institute of Mathematics, Statistics, and Scientific Computing, Univ. of Campinas, Campinas, SP 13083-859, Brazil. ORCID: .

5. Assistant Professor, School of Technology, Univ. of Campinas, Limeira, SP 13484-332, Brazil. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JHEND8.HYENG-13748

Reference28 articles.

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2. Askar M. K. and K. K. Al-Jumaily. 2008. “A nonlinear optimization model for estimating Manning’s roughness coefficient.” In Proc. 12th Int. Water Technology Conf. IWTC12 1299–1306. Alexandria Egypt: An-Najah National Univ.

3. A linked simulation–optimization model for simultaneously estimating the Manning’s surface roughness values and their parameter structures in shallow water flows

4. Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients

5. Maximum likelihood estimation for the drift parameter in diffusion processes