Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/3834
Título: Assessment of data-driven modeling strategies for water delivery canals
Autor: Tavares, Isaias
Borges, José
Mendes, Mário J. G. C.
Botto, Miguel Ayala
Palavras-chave: Nonlinear Modeling
Open-Channel Water Delivery Systems
Artificial Neural Networks
Composite Local Linear Models
Fuzzy Systems
Data: Set-2013
Editora: Springer
Citação: TAVARES, Isaías, [et al] - Assessment of data-driven modeling strategies for water delivery canals. Neural Computing and Applications. Vol. 23, nr. 3-4 (2013), p. 625-633.
Relatório da Série N.º: SI
Resumo: The aim of this paper is to develop models for experimental open-channel water delivery systems and assess the use of three data-driven modeling tools toward that end. Water delivery canals are nonlinear dynamical systems and thus should be modeled to meet given operational requirements while capturing all relevant dynamics, including transport delays. Typically, the derivation of first principle models for open-channel systems is based on the use of Saint-Venant equations for shallow water, which is a time-consuming task and demands for specific expertise. The present paper proposes and assesses the use of three data-driven modeling tools: artificial neural networks, composite local linear models and fuzzy systems. The canal from Hydraulics and Canal Control Nucleus (A parts per thousand vora University, Portugal) will be used as a benchmark: The models are identified using data collected from the experimental facility, and then their performances are assessed based on suitable validation criterion. The performance of all models is compared among each other and against the experimental data to show the effectiveness of such tools to capture all significant dynamics within the canal system and, therefore, provide accurate nonlinear models that can be used for simulation or control. The models are available upon request to the authors.
Peer review: yes
URI: http://hdl.handle.net/10400.21/3834
DOI: 10.1007/s00521-013-1417-8
ISSN: 0941-0643
Versão do Editor: http://link.springer.com/article/10.1007%2Fs00521-013-1417-8
Aparece nas colecções:ISEL - Eng. Mecan. - Artigos



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