Estimation of the beach bar parameters using the genetic algorithms

Abstract Waves, topographic characteristics and material properties are the most significant factors, which affect the sediment movement and coastal profiles. In this study, considering the wave height ( H 0 ) and period ( T ), the bed slope ( m ) and the sediment diameter ( d 50 ), the cross-shore sediment movement is investigated using a physical model and obtained 80 experimental data for offshore bar geometric parameters. The experimental results are also evaluated by the genetic algorithms (GAs) that are limitedly employed in coastal engineering applications. The results of GAs model and equations cited in the literature are compared with the experimental results. It is concluded that estimates of bar parameters by the GAs give a better estimation performance with respect to other conventional methods.

[1]  Dong-Sheng Jeng,et al.  Application of artificial neural networks in tide-forecasting , 2002 .

[2]  Mehmet Özger,et al.  Temporal significant wave height estimation from wind speed by perceptron Kalman filtering , 2004 .

[3]  M. C. Deo,et al.  Forecasting wind with neural networks , 2003 .

[4]  Paul Schonfeld,et al.  Scheduling Interdependent Waterway Projects Through Simulation and Genetic Optimization , 2005 .

[5]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[6]  Vladimir M. Krasnopolsky,et al.  Neural network approximations for nonlinear interactions in wind wave spectra: direct mapping for wind seas in deep water , 2005 .

[7]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[8]  A. Cheng,et al.  Optimal Management in Saltwater-Intruded Coastal Aquifers By Simple Genetic Algorithm , 2003 .

[9]  David Coley,et al.  Introduction to Genetic Algorithms for Scientists and Engineers , 1999 .

[10]  Li-Ching Lin,et al.  Multi-point tidal prediction using artificial neural network with tide-generating forces , 2006 .

[11]  Kevin M. Passino,et al.  Genetic adaptive state estimation , 2000 .

[12]  Tsong-Lin Lee.  Back-propagation neural network for long-term tidal predictions , 2004 .

[13]  Nicholas C. Kraus,et al.  Evaluation of Beach Erosion and Accretion Predictors , 1991 .

[14]  Daniel T. Cox,et al.  Water Level Observations and Short-Term Predictions Including Meteorological Events for Entrance of Galveston Bay, Texas , 2002 .

[15]  Anne S. Kiremidjian,et al.  Ship Traffic Modeling Methodology for Ports , 2003 .

[16]  Seree Supharatid,et al.  Field Data Recovery of Tidal Level Using a Neuro-Genetic Algorithm , 2004 .

[17]  H. K. Cigizoglu,et al.  Artificial intelligence methods in breakwater damage ratio estimation , 2005 .

[18]  Josep R. Medina,et al.  Discussion of "Predictions of Missing Wave Data by Recurrent Neuronets" , 2004 .

[19]  Mehmet Özger,et al.  Prediction of wave parameters by using fuzzy logic approach , 2007 .

[20]  堀川 清司,et al.  Nearshore dynamics and coastal processes : theory, measurement, and predictive models , 1988 .

[21]  Murat İhsan Kömürcü,et al.  Determination of bar parameters caused by cross-shore sediment movement , 2007 .

[22]  B. G. Ruessink,et al.  Nearshore bar crest location quantified from time-averaged X-band radar images , 2002 .

[24]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[25]  R. Deigaard,et al.  Onshore/Offshore Sediment Transport and Morphological Modelling of Coastal Profiles , 1991 .

[26]  Tl Lee.  Neural network prediction of a storm surge , 2006 .

[27]  M. Larson.  Model of Beach Profile Change Under Random Waves , 1996 .

[28]  I. Leont’yev.  Numerical modelling of beach erosion during storm event , 1996 .

[29]  O. Makarynskyy,et al.  Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia , 2004 .

[30]  Vijay K. Agarwal,et al.  RBF network for spatial mapping of wave heights , 2005 .

[31]  Kemal Günaydın,et al.  Investigation of Offshore Bar Geometry Under Regular and Irregular Waves , 2005 .

[32]  Dong Hyawn Kim,et al.  Neural network for design and reliability analysis of rubble mound breakwaters , 2005 .

[33]  Nicholas C. Kraus,et al.  Development of a regional neural network for coastal water level predictions , 2003 .

[34]  Makarand Deo,et al.  Analysis of Wave Directional Spreading Using Neural Networks , 2002 .

[35]  R. Hallermeier.  USES FOR A CALCULATED LIMIT DEPTH TO BEACH EROSION , 1978 .

[36]  Robert G. Dean,et al.  Numerical models and intercomparisons of beach profile evolution , 1997 .

[37]  M. Kabdaşli,et al.  Cross-Shore Sorting on a Beach under Wave Action , 2006 .

[38]  Subba Rao,et al.  Hindcasting of storm waves using neural networks , 2005 .

[39]  Vladimir M. Krasnopolsky,et al.  A neural network technique to improve computational efficiency of numerical oceanic models , 2002 .

[40]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[41]  Tai-Wen Hsu.  Geometric characteristics of storm-beach profiles caused by inclined waves , 1998 .