Forecasting hurricane wave height in Gulf of Mexico using soft computing methods

Abstract The main objective of this paper was to use different soft computing methods to forecast hurricane wave height over Gulf of Mexico. For this purpose, three well-known soft computing methods; Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) were used. The friction velocity was also used instead of wind speed at 10 m height. For this case, three different assumptions were considered for computing friction velocity. For the analysis, one month (from August 20th, 2008 to September 20th, 2008) data including passage of hurricanes Gustav and Ike over Gulf of Mexico were collected from six different buoys. Next, seven different parameters were used to train the models based on the data observed from five buoys. Later the trained models were used to forecast observed wave height in the sixth buoy in different lead times. The results showed that using more number of input parameters leads to better performance of the methods, especially in longer lead times. Moreover, in longer lead times, the effect of other parameters such as air pressure were increased and assisted the models to outperform previous models. In addition; different assumptions for friction velocity eventuated comparable results.

[1]  I. Young,et al.  Hurricane generated waves as observed by satellite , 1996 .

[2]  O. Makarynskyy,et al.  Improving wave predictions with artificial neural networks , 2004 .

[3]  Chang Lin,et al.  Neural network for wave forecasting among multi-stations , 2002 .

[4]  Amir Etemad-Shahidi,et al.  An alternative approach for the prediction of significant wave heights based on classification and regression trees , 2008 .

[5]  Mark D. Powell,et al.  Wind and waves in extreme hurricanes , 2012 .

[6]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .

[7]  Lie-Yauw Oey,et al.  Hindcast of Waves and Currents in Hurricane Katrina , 2008 .

[8]  W. Munk,et al.  Wind, sea, and swell : Theory of relations for forecasting , 1947 .

[9]  C. L. Bretschneider.  REVISED WAVE FORECASTING RELATIONSHIPS , 2000 .

[10]  M. Powell,et al.  Reduced drag coefficient for high wind speeds in tropical cyclones , 2003, Nature.

[11]  Abdullah Al Mamun,et al.  Regional ocean wave height prediction using sequential learning neural networks , 2017 .

[12]  Somayeh Mafi,et al.  Wind and wave energy potential in southern Caspian Sea using uncertainty analysis , 2017 .

[13]  Marina Fruehauf.  Shore Protection Manual , 2016 .

[14]  J. Mahjoobi,et al.  Prediction of significant wave height using regressive support vector machines , 2009 .

[15]  H. Nagy,et al.  Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model , 2002 .

[16]  R. Weisberg,et al.  Hurricane storm surge simulations comparing three-dimensional with two-dimensional formulations based on an Ivan-like storm over the Tampa Bay, Florida region , 2008 .

[17]  M. H. Kazeminezhad,et al.  Prediction formula for longshore sediment transport rate with M5' algorithm , 2016 .

[18]  M. H. Kazeminezhad,et al.  Hindcasting of wave parameters using different soft computing methods , 2008 .

[19]  Jin Wu.  Wind‐stress coefficients over sea surface from breeze to hurricane , 1982 .

[20]  W. Large,et al.  Open Ocean Momentum Flux Measurements in Moderate to Strong Winds , 1981 .

[21]  Mohammad Reza Nikoo,et al.  Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction , 2011 .

[22]  Pedro Antonio Gutiérrez,et al.  Significant wave height and energy flux range forecast with machine learning classifiers , 2015, Eng. Appl. Artif. Intell..

[23]  I. Young,et al.  Observations of the spectra of hurricane generated waves , 1998 .

[24]  Sancho Salcedo-Sanz,et al.  Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach , 2016 .

[25]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[26]  M. Donelan,et al.  Dynamics and Modelling of Ocean Waves , 1994 .

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

[28]  I. Young,et al.  A review of the sea state generated by hurricanes , 2003 .

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

[30]  Wei Zhao,et al.  Directional Wind-Wave Coupling in Fully Coupled Atmosphere-Wave-Ocean Models: Results from CBLAST-Hurricane , 2013 .

[31]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[32]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[33]  A. Etemad-Shahidi,et al.  COMPARISON BETWEEN M5 MODEL TREE AND NEURAL NETWORKS FOR PREDICTION OF SIGNIFICANT WAVE HEIGHT IN LAKE SUPERIOR , 2009 .

[34]  Sancho Salcedo-Sanz,et al.  Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface , 2015 .

[35]  Clint Dawson,et al.  Performance of the Unstructured-Mesh, SWAN+ADCIRC Model in Computing Hurricane Waves and Surge , 2011, Journal of Scientific Computing.

[37]  Dina Makarynska,et al.  Artificial neural networks in wave predictions at the west coast of Portugal , 2005, Comput. Geosci..

[38]  H. Md. Azamathulla,et al.  Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways , 2012, Neural Computing and Applications.

[39]  Tal Ezer,et al.  Loop Current warming by Hurricane Wilma , 2006 .

[40]  J. C. Dietrich,et al.  Hurricane Gustav (2008) Waves and Storm Surge: Hindcast, Synoptic Analysis, and Validation in Southern Louisiana , 2011 .

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

[42]  Seyed Jamshid Mousavi,et al.  APPLICATION OF FUZZY INFERENCE SYSTEM IN THE PREDICTION OF WAVE PARAMETERS , 2005 .

[43]  Jeffrey A. Melby,et al.  An application of Boussinesq modeling to Hurricane wave overtopping and inundation , 2009 .

[44]  J. Vimala,et al.  Real Time wave forecasting using artificial neural network with varying input parameter , 2014 .

[45]  Andrew B. Kennedy,et al.  Observations of Landfalling Wave Spectra during Hurricane Ike , 2011 .

[46]  Marcel Zijlema,et al.  Gulf of Mexico hurricane wave simulations using SWAN: Bulk formula-based drag coefficient sensitivity for Hurricane Ike , 2012 .

[47]  Ahmadreza Zamani,et al.  Learning from data for wind–wave forecasting , 2008 .

[48]  Mehmet Özger,et al.  Significant wave height forecasting using wavelet fuzzy logic approach , 2010 .

[49]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[50]  Qin Chen,et al.  Directional spectra of hurricane‐generated waves in the Gulf of Mexico , 2011 .

[51]  Ming Ye,et al.  Estimation of saturated hydraulic conductivity from double‐ring infiltrometer measurements , 2016 .

[52]  M. H. Kazeminezhad,et al.  Wave height forecasting in Dayyer, the Persian Gulf , 2011 .

[53]  Y. Goda.  Revisiting Wilson’s Formulas for Simplified Wind-Wave Prediction , 2003 .

[54]  S. Mafi,et al.  Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS , 2017 .