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A comparative study for estimation of wave height using traditional and hybrid soft-computing methods

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Abstract

The present study developed a wave height prediction model by the recorded climatic data. We used 1-year buoy data for training and testing the developed soft-computing model. Models were developed using a novel method based on the Support Vector Machine (SVM) coupled with the Firefly Algorithm (FFA). This research work used the FFA for estimating the optimum parameters. In addition, this work compared the predicted results of SVM-FFA model to the artificial neural networks (ANNs) and genetic programming (GP). The results indicate that the SVM-FFA approach attains an improvement in capability of generalization and predictive accuracy in comparison to the GP and ANN. A thorough statistical analysis was conducted to compare the predictions of three models i.e., among the SVM-FFA, ANN, and GP. A high R 2 value of 0.979 was obtained for the SVM-FFA predictions. Further, the ANN and GP results showed R 2 values of 0.524 and 0.525, respectively. Moreover, achieved results indicate that the developed SVM-FFA model can be used with confidence for future research works on formulating novel models for predictive strategy on wave height. The results also show that the new algorithm can learn thousands of times faster than the former popular learning algorithms. This study finds that the application of SVM-FFA is the likely alternative method for estimating the wave height.

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References

  • Abed-Elmdoust A, Kerachian R (2012) Wave height prediction using the rough set theory. Ocean Eng 54:244–250

    Article  Google Scholar 

  • Alexandre E, Cuadra L, Nieto-Borge J, Candil-García G, del Pino M, Salcedo-Sanz S (2015) A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction. Ocean Model 92:115–123

    Article  Google Scholar 

  • Altunkaynak A, Wang K-H (2012) Estimation of significant wave height in shallow lakes using the expert system techniques. Expert Syst Appl 39:2549–2559. doi:10.1016/j.eswa.2011.08.106

    Article  Google Scholar 

  • Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318:7–16

    Article  Google Scholar 

  • Assareh E, Behrang M, Assari M, Ghanbarzadeh A (2010) Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35:5223–5229

    Article  Google Scholar 

  • Azzellino A, Ferrante V, Kofoed JP, Lanfredi C, Vicinanza D (2013) Optimal siting of offshore wind-power combined with wave energy through a marine spatial planning approach. Int J Mar Energy 3:e11–e25

    Article  Google Scholar 

  • Babovic V, Keijzer M (2002) Rainfall Runoff Modelling Based on Genetic Programming. Hydrol Res 33(5):331–346. http://hr.iwaponline.com/content/33/5/331

  • Battjes JA (1974) Computation of set-up, longshore currents, run-up and overtopping due to wind-generated waves. TU Delft, Delft University of Technology

  • Burges CJC (1998) A Tutorial on Support Vector Machines for Pattern Recognition. Data Min Knowl Discov 2(2):121–167. doi:10.1023/A:1009715923555

  • Ch S et al (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288

    Article  Google Scholar 

  • Chung K-M, Kao W-C, Sun C-L, Wang L-L, Lin C-J (2003) Radius margin bounds for support vector machines with the RBF kernel. Neural Comput 15:2643–2681

    Article  Google Scholar 

  • Collobert R, Bengio S (2000) Support vector machines for large-scale regression problems. Institut Dalle Molle d’Intelligence Artificelle Perceptive (IDIAP), Martigny, Switzerland, Tech Rep IDIAP-RR-00-17

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. doi:10.1007/BF00994018

  • Dorigo M, Stützle T (2003) The ant colony optimization metaheuristic: Algorithms, applications, and advances. In: Handbook of metaheuristics. Springer, pp 250–285

  • Fister I, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  • Friedrichs F, Igel C (2005) Evolutionary tuning of multiple SVM parameters. Neurocomputing 64:107–117

    Article  Google Scholar 

  • Gocić M, Motamedi S, Shamshirband S, Petković D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput Electron Agric 113:164–173

    Article  Google Scholar 

  • Govindaraju RS (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115–123

    Article  Google Scholar 

  • Huang C, Davis L, Townshend J (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749

    Article  Google Scholar 

  • Jain P, Garibaldi JM, Hirst JD (2009) Supervised machine learning algorithms for protein structure classification. Comput Biol Chem 33:216–223

    Article  Google Scholar 

  • Jay S (2010) Built at sea: Marine management and the construction of marine spatial planning. Town Plann Rev 81:173–192

    Article  Google Scholar 

  • Ji Y, Sun S (2013) Multitask multiclass support vector machines: model and experiments. Pattern Recognit 46:914–924

    Article  Google Scholar 

  • Jiang X, Lu W, Zhao H, Yang Q, Chen M (2014) Quantitative evaluation of mining geo-environmental quality in Northeast China: comprehensive index method and support vector machine models. Environ Earth Sci 73(12):7945–7955. doi:10.1007/s12665-014-3953-7

    Article  Google Scholar 

  • Kaliraj S, Chandrasekar N, Magesh N (2014) Impacts of wave energy and littoral currents on shoreline erosion/accretion along the south-west coast of Kanyakumari, Tamil Nadu using DSAS and geospatial technology. Environ Earth Sci 71:4523–4542

    Article  Google Scholar 

  • Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civ Eng 8:201–220

    Article  Google Scholar 

  • Khu ST, Liong SY, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forecasting 1 Jawra. J Am Water Resour Assoc 37:439–451

    Article  Google Scholar 

  • Kisi O, Shiri J, Karimi S, Shamshirband S, Motamedi S, Petković D, Hashim R (2015) A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl Math Comput 270:731–743. doi:10.1016/j.amc.2015.08.085

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection vol 1. MIT press

  • Lee S-W, Verri A (2003) Support vector machines for computer vision and pattern recognition. World Scientific

  • Li L, Wu K, Zhou D-W (2014) Extraction algorithm of mining subsidence information on water area based on support vector machine. Environ Earth Sci 72:3991–4000

    Article  Google Scholar 

  • Lorena AC, De Carvalho AC (2008) Evolutionary tuning of SVM parameter values in multiclass problems. Neurocomputing 71:3326–3334

    Article  Google Scholar 

  • Lu W-Z, Wang W-J (2005) Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere 59:693–701

    Article  Google Scholar 

  • Mahjoobi J, Adeli Mosabbeb E (2009) Prediction of significant wave height using regressive support vector machines. Ocean Eng 36:339–347. doi:10.1016/j.oceaneng.2009.01.001

    Article  Google Scholar 

  • Mahjoobi J, Etemad-Shahidi A, Kazeminezhad MH (2008) Hindcasting of wave parameters using different soft computing methods. Appl Ocean Res 30:28–36. doi:10.1016/j.apor.2008.03.002

    Article  Google Scholar 

  • Makarynskyy O (2004) Improving wave predictions with artificial neural networks. Ocean Eng 31:709–724. doi:10.1016/j.oceaneng.2003.05.003

    Article  Google Scholar 

  • Maresca S, Braca P, Horstmann J, Grasso R (2014) Maritime surveillance using multiple high-frequency surface-wave radars. IEEE Trans Geosci Remote Sens 52:5056–5071

    Article  Google Scholar 

  • Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IEEE 2002. IJCNN’02, 2002, pp 1702–1707

  • Ornella L, Tapia E (2010) Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data. Comput Electron Agric 74:250–257. doi:10.1016/j.compag.2010.08.013

    Article  Google Scholar 

  • Özger M (2010) Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Eng 37:1443–1451. doi:10.1016/j.oceaneng.2010.07.009

    Article  Google Scholar 

  • Pal SK, Rai C, Singh AP (2012) Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Int J Intell Syst Appl (IJISA) 4:50

    Google Scholar 

  • Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35:1578–1587

    Article  Google Scholar 

  • Ren F, Wu X, Zhang K, Niu R (2015) Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges. China Environ Earth Sci 73:4791–4804

    Article  Google Scholar 

  • Shamshirband S et al (2014) Wind turbine power coefficient estimation by soft computing methodologies: comparative study. Energy Convers Manag 81:520–526

    Article  Google Scholar 

  • Shamshirband S, Mohammadi K, Tong CW, Zamani M, Motamedi S, Ch S (2015) A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor Appl Climatol 1–13. doi:10.1007/s00704-015-1482-2

  • Singh SK, Srivastava PK, Gupta M, Thakur JK, Mukherjee S (2014) Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environ Earth Sci 71:2245–2255

    Article  Google Scholar 

  • Singhal G, Panchang VG, Nelson JA (2013) Sensitivity assessment of wave heights to surface forcing in Cook Inlet. Alaska Cont Shelf Res 63:S50–S62

    Article  Google Scholar 

  • Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23:2031–2038

    Article  Google Scholar 

  • Sung AH, Mukkamala S (2003) Identifying important features for intrusion detection using support vector machines and neural networks. In: Proceedings. 2003 Symposium on, IEEE Applications and the Internet, 2003, pp 209–216

  • Thirumalaiah K, Deo MC (2000) Hydrological forecasting using neural networks. J Hydrol Eng 5:180–189

    Article  Google Scholar 

  • Vapnik V (2000) The nature of statistical learning theory. Springer

  • Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 2. Wiley, New York

    Google Scholar 

  • Whitley D, Starkweather T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Comput 14(3):347–361. doi:10.1016/0167-8191(90)90086-O

  • Wu K-P, Wang S-D (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognit 42:710–717

    Article  Google Scholar 

  • Wu X, Ren F, Niu R (2014) Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environ Earth Sci 71:4725–4738

    Article  Google Scholar 

  • Xu Y, Cheng C, Zhang Y, Zhang D (2014) Identification of algal blooms based on support vector machine classification in Haizhou Bay. East China Sea Environ Earth Sci 71:475–482

    Article  Google Scholar 

  • Yang XS (2009) Stochastic Algorithms: Foundations and Applications: In: Watanabe O, Zeugmann T (eds) 5th International Symposium, Sapporo, Japan, October 2009. Proceedings, Springer Berlin Heidelberg, pp 169–178. http://doi.org/10.1007/978-3-642-04944-6_14

  • Yang XS (2010) Research and Development in Intelligent Systems XXVI. In: Bramer M, Ellis R, Petridis M (eds) Incorporating Applications and Innovations in Intelligent Systems XVII, Springer London, pp 209–218 doi:10.1007/978-1-84882-983-1_15

  • Yang X-S (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29:175–184

    Article  Google Scholar 

  • Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169–174

    Article  Google Scholar 

  • Yang H, Huang K, King I, Lyu MR (2009) Localized support vector regression for time series prediction. Neurocomputing 72:2659–2669

    Article  Google Scholar 

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Acknowledgments

The authors are thankful to the support received from the HIR-MOHE office, University of Malaya under Grant No. UM.C/HIR/MOHE/ENG/34.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chandrabhushan Roy, Shervin Motamedi & Roslan Hashim

  2. Institute of Ocean and Earth Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shervin Motamedi & Roslan Hashim

  3. Department of Computer System and Information Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband

  4. Department for Mechatronics and Control, Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, 18000, Nis, Serbia

    Dalibor Petković

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  1. Chandrabhushan Roy
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Correspondence to Roslan Hashim or Shahaboddin Shamshirband.

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Roy, C., Motamedi, S., Hashim, R. et al. A comparative study for estimation of wave height using traditional and hybrid soft-computing methods. Environ Earth Sci 75, 590 (2016). https://doi.org/10.1007/s12665-015-5221-x

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