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- Assessment of data-driven modeling strategies for water delivery canalsPublication . Tavares, Isaias; Borges, José; Gonçalves Cavaco Mendes, Mário José; Ayala Botto, MiguelThe 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.
- Assessment of data-driven modeling strategies for water delivery canalsPublication . Tavares, Isaías; Borges, José; Gonçalves Cavaco Mendes, Mário José; Ayala Botto, MiguelThe aim of this work is to develop nonlinear dynamical models for the canal system of Núcleo de Hidráulica e Controlo de Canais. The canal is a nonlinear system and thus should be modeled to meet given operational requirements, while capturing all relevant system dynamics, such as the resonance waves created due to the movements of gates, and also contributing to the controller precision. The nonlinear modeling is based on data-driven methods, namely Composite Local Linear Models, Fuzzy Models and Artificial Neural Networks. These models are identified using data collected from the experimental facility, and their performance is assessed based on suitable validation criteria. The modeling results show the effectiveness of these models while capturing all significant dynamics for the canal system.