Hybrid sediment transport model for the “linguado” channel, state of Santa Catarina, Brazil

Main Article Content

Edison Conde Perez dos Santos
Carlos Alberto Nunes Cosenza
José Carlos Cesar Amorim
صندلی اداری


This study involves an assessment of various artificial intelligence-related techniques which aim to produce a more robust system for sediment transport modeling. The intelligent systems developed in this research are directly applicable to academic knowledge and use data from a report on "water circulation assessment in the “Linguado” Channel and Babitonga Bay ,”Santa Catarina”, Brazil, developed by  Military Engineering Institute (IME). The solution employed for sediment transport was built using an intelligent system from the conception of two hybrid models. The first was a Neuro-Fuzzy (ANFIS) hybrid model for the study of hydrodynamic behavior, aiming to determine flow rate in the channel. The second was a fuzzy genetic model, able to assess sediment transport in the “Linguado” Channel. The study's conclusion compares the different effects involved in the dredging equilibrium in the “Linguado” Channel according to this hybrid model with the results obtained using a finite element model in the MIKE21® software.


Download data is not yet available.

Article Details



ASSAD, L. P. F. et al. (2009) Basic course on computational hydrodynamic modeling and dispersion of pollutants, May 2009, Laboratory of Computational Methods - UFRJ.

BRAIT, M.; DALZOTTO, D. (2010) Numeric Simulations Shall Waters, CNMAC, p. 10-26.

CHOONG, J. (2009) Build Neural Network With MS Excel ®, Published by XLPert Enterprise Copyright © 2009 by XLPert Enterprise.

FEMAR, (2000) Catalog of Brazilian Maregraphic Stations . Fundação de Estudos do Mar. Rio de Janeiro, p. 281, RJ.

GRACEA, V. B.; MAS-PLAC, J.; NOVAISB, T. O.; SACCHID, E.; ZUPPIA, G. M. (2008) Hydrological mixing and geochemical processes characterization in an estuarine/mangrove system using environmental tracers in Babitonga Bay (Santa Catarina, Brazil), v. 28, p. 682–695.

HERKENHOFF, L.; FOGLI, J. (2013) Applied Statistics for Business and Management using Microsoft Excel, © Springer Science, New York, ISBN 978-1-4614-8422-6.

IME & UNIVALE. (2003) Diagnóstico dos estudos de circulação de água no canal do “Linguado” e na Baía de Babitonga/SC.

JANG, J. S. R. (1993) ANFIS: Adaptive – Network - Based Fuzzy Inference System. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, v. 23, n. 3, May/Jun.

JANG, J. S. R.; SUN, C. T.; MIZUTANI, E. (1997) Neuro-Fuzzy and Soft computing – A computational approaching learning and machine intelligence, Prentice Hall.

KLEMENS A. L. A. (2012) Environmental legislation with the focus of conflicts: an analysis from the social representations of the nature of small farmers in Minas Gerais, Brasil, Soc. & Nat., Uberlândia, v. 24, n. 3, p. 405-418, Sept/Dec.

REZENDE S. O. et al. (2005) Intelligent Systems - Fundamentals and Applications. Manole ltd.

RODRIGUES A. M. T. (2000) Socio-Economic Diagnosis and the environmental perception of communities of artisanal fishermen from the Bay of Babitonga: a subsidy to coastal management. UFSC / Environment Department.

ROSS, T. J. (2004) Fuzzy logic with Engineering application, 2nd edition, John & Willey Sons Ltd.

TRIOLA, M. F. (1999) Introduction to Statistics. LTC. 7th. ed. Rio de Janeiro.

VIEIRA C. V. et al. (2008) Morphosedimentary characterization and sectorization of the estuary complex of Babitonga Bay. Geosciences Paranaense Publication, UFPR, v. 62-63, p. 85-105.

RUSSEL S.; NORVIG P. (2008) Artificial intelligence, a modern approach. Pearson Prentice Hall, 2nd ed., Madrid.

فروشگاه اینترنتی