nach oben

Environmental Earth Sciences

Erschienen in:

01.04.2016 | Original Article

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

verfasst von: Chandrabhushan Roy, Shervin Motamedi, Roslan Hashim, Shahaboddin Shamshirband, Dalibor Petković

Erschienen in: Environmental Earth Sciences | Ausgabe 7/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Impacts of anthropogenic activities on weathering and carbon fluxes: a case study in the Xijiang River basin, southwest China

Nächster Artikel Erosion rates of different land uses and sediment sources in a watershed using the 137Cs tracing method: field studies in the Loess Plateau of China

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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

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 92:115–123CrossRef

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 CrossRef

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

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 35:5223–5229CrossRef

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–e25CrossRef

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 129:279–288CrossRef

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 15:2643–2681CrossRef

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 13:34–46CrossRef

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

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 113:164–173CrossRef

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

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

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

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

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

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 CrossRef

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 71:4523–4542CrossRef

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

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 37:439–451CrossRef

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 CrossRef

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 72:3991–4000CrossRef

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

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

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 CrossRef

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 CrossRef

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

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

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 CrossRef

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 CrossRef

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

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

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 73:4791–4804CrossRef

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

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 71:2245–2255CrossRef

Singhal G, Panchang VG, Nelson JA (2013) Sensitivity assessment of wave heights to surface forcing in Cook Inlet. Alaska Cont Shelf Res 63:S50–S62CrossRef

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

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 5:180–189CrossRef

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

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

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 42:710–717CrossRef

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 71:4725–4738CrossRef

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 71:475–482CrossRef

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 29:175–184CrossRef

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

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

Titel: A comparative study for estimation of wave height using traditional and hybrid soft-computing methods
verfasst von: Chandrabhushan Roy
Shervin Motamedi
Roslan Hashim
Shahaboddin Shamshirband
Dalibor Petković
Publikationsdatum: 01.04.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 7/2016
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-015-5221-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 7/2016

Topographical and rainfall factors determining the formation of gully-type debris flows caused by shallow landslides in the Dayi area, Guizhou Province, China

Aeolian sand transport above three desert surfaces in northern China with different characteristics (shifting sand, straw checkerboard, and gravel): field observations

The influence of diapir brine on groundwater quality of surrounding aquifers, Larestan, Iran

Self-organized criticality of significant fording landslides in Three Gorges Reservoir area, China

Geochemical processes and multivariate statistical analysis for the assessment of groundwater quality in the Swarnamukhi River basin, Andhra Pradesh, India

Effect of phytoremediation on concentrations of benzene, toluene, naphthalene, and dissolved oxygen in groundwater at a former manufactured gas plant site, Charleston, South Carolina, USA, 1998–2014