Hindcasting Long Waves in a Port: An ANN Approach

Infragravity waves play an important role in port operations and many nearshore processes, and therefore their characterization is of major interest for oceanographers and coastal engineers. The lack of proper measurement networks and historic databases makes the development of hindcasting techniques essential. This work presents a fully developed infragravity wave hindcast methodology through Artificial Neural Networks (ANNs) and its application to a case study. The characteristic wave-heights of the low frequency band and the swell band inside a port basin are computed for a period of eight years on the basis of the long-term offshore wave conditions, a short record of searlevel oscillations and the historic tidal harmonic constituents. Based on the results, we construct and analyze the single and the joint probability density functions of the two characteristic wave-heights studied. In addition, we study the relationships between the infragravity energy inside the port and the offshore wave parameters and explore the extreme events during which the low frequency band energy exceeds the swell energy. The findings highlight the potential of the methodology to characterize the infragravity wave conditions inside a port basin and its suitability to study other coastal problems in which these waves are involved.

[1]  Leopoldo Franco,et al.  Measurement of long waves at the harbor of Marina di Carrara, Italy , 2011 .

[2]  N. Booij,et al.  A third-generation wave model for coastal regions-1 , 1999 .

[3]  Ap van Dongeren,et al.  Spectral wave-driven sediment transport across a fringing reef , 2015 .

[4]  S. Lentz,et al.  Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE , 2002 .

[5]  Leopoldo Franco,et al.  Harbour resonance at Marina di Carrara: linear and non linear aspects , 2009 .

[6]  Jurjen A. Battjes,et al.  Seiche characteristics of Rotterdam Harbour , 2004 .

[7]  N. Booij,et al.  A third‐generation wave model for coastal regions: 2. Verification , 1999 .

[8]  Toshiyuki Asano,et al.  External Forces of Sediment Transport in Surf and Swash Zones Induced by Wave Groups and Their Associated Long Waves , 2007 .

[9]  Charitha Pattiaratchi,et al.  Observations of infragravity period oscillations in a small marina , 2014 .

[10]  Robert T. Guza,et al.  OBSERVATIONS OF SEICHE FORCING AND AMPLIFICATION IN THREE SMALL HARBORS , 1996 .

[11]  E. C. Bowers.  Low Frequency Waves in Intermediate Water Depths , 1993 .

[12]  Giorgio Bellotti,et al.  Transient response of harbours to long waves under resonance conditions , 2007 .

[13]  Nitai Drimer,et al.  Prediction of Long Forcing Waves for Harbor Agitation Studies , 2006 .

[14]  Farid U. Dowla,et al.  Backpropagation Learning for Multilayer Feed-Forward Neural Networks Using the Conjugate Gradient Method , 1991, Int. J. Neural Syst..

[15]  G. Cats,et al.  The Hirlam project [meteorology] , 1996 .

[16]  Carolyn Harris,et al.  Infragravity currents in a small ría: Estuary-amplified coastal edge waves? , 2014 .

[17]  Tom E. Baldock,et al.  Sediment transport and beach morphodynamics induced by free long waves, bound long waves and wave groups , 2010 .

[18]  Philip L.-F. Liu,et al.  Finite-Element Model for Modified Boussinesq Equations. II: Applications to Nonlinear Harbor Oscillations , 2004 .

[19]  Ryan J. Lowe,et al.  Numerical modeling of low-frequency wave dynamics over a fringing coral reef , 2013 .

[20]  Masayoshi Kubo,et al.  A Study on Prediction System of Critical Wave Conditions for Ship Mooring Against Typhoons , 2004 .

[21]  J. A. Battjes,et al.  Long waves induced by short-wave groups over a sloping bottom , 2003 .

[22]  Gregorio Iglesias,et al.  Headland-bay beach planform and tidal range: A neural network model , 2009 .

[23]  Seree Supharatid.  Tidal-Level Forecasting and Filtering by Neural Network Model , 2003 .

[24]  Marcel R. A. van Gent,et al.  Wave Runup on Dikes with Shallow Foreshores , 2001 .

[25]  J. Shao.  Linear Model Selection by Cross-validation , 1993 .

[26]  Obdulia Fernández de Ybarra,et al.  Implications of long waves in harbor management: The Gijón port case study , 2008 .

[27]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[28]  Juan R. Rabuñal,et al.  Performance of artificial neural networks in nearshore wave power prediction , 2014, Appl. Soft Comput..

[29]  G. Iglesias,et al.  Artificial Intelligence for estimating infragravity energy in a harbour , 2013 .

[30]  Wim van der Molen,et al.  Numerical Simulation of Long-Period Waves and Ship Motions in Tomakomai Port, Japan , 2006 .

[31]  Gregorio Iglesias,et al.  Long period oscillations and tidal level in the Port of Ferrol , 2012 .

[32]  N. Plant,et al.  Two-dimensional time dependent hurricane overwash and erosion modeling at Santa Rosa Island , 2010 .

[33]  Mario Lopez,et al.  Long wave effects on a vessel at berth , 2014 .

[34]  Charitha Pattiaratchi,et al.  Influence of offshore topography on infragravity period oscillations in Two Rocks Marina, Western Australia , 2014 .