References
- Al-Mashan N, Jradi A, Aldasthi H, Neelamani S. 2019. The extreme waves in Kuwaiti territorial waters using measured data. Ocean Eng. 190.
- Alvise B, Barbariol F, Bergamasco F, Carniel S, Sclavo M. 2017. Space-time extreme wind waves: analysis and prediction of shape and height. Ocean Model. 113:201–216.
- Anam K, Al-Jumaily A. 2017. Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw. 85:51–68.
- Atkinson AC, Hawkins DM. 2006. Identification of outliers. Biometrics. 37(4):860.
- Benetazzo A, Barbariol F, Pezzutto P, Staneva J, Behrens A, Davison S, Bergamasco F, Sclavo M, Cavaleri L. 2021. Towards a unified framework for extreme sea waves from spectral models: rationale and applications. Ocean Eng. 219.
- Breuniq MM, Kriegel H-P, Ng RT, Sander J. 2000. LOF: identifying density-based local outliers. SIGMOD Rec. 29(2):93–104. http://www.scopus.com/inward/record.url?eid=2-s2.0-0039253819&partnerID=40&md5=8237238cd72e69d886fad873ae89c433.
- Camus P, Herrera S, Gutiérrez JM, Losada IJ. 2019. Statistical downscaling of seasonal wave forecasts. Ocean Model. 138:1–12.
- Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A. 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ. 651:2087–2096.
- Dean R. 1990. Abnormal waves: A possible explanation. Water Wave Kinemat. 609–612.
- Demetriou D, Michailides C, Papanastasiou G, Onoufriou T. 2021. Coastal zone significant wave height prediction by supervised machine learning classification algorithms. Ocean Eng. 221.
- den Bieman JP, van Gent MRA, van den Boogaard HFP. 2020. Wave overtopping predictions using an advanced machine learning technique. Coast Eng. 166:103830.
- Dixit P, Londhe S. 2016. Prediction of extreme wave heights using neuro wavelet technique. Appl Ocean Res. 58:241–252.
- Emmanouil S, Aguilar SG, Nane GF, Schouten JJ. 2020. Statistical models for improving significant wave height predictions in offshore operations. Ocean Eng. 206.
- Guedes Soares C, Cherneva Z, Antao EM. 2003. Characteristics of abnormal waves in North Sea storm sea states. Appl Ocean Res. 25(6):337–344.
- Huang GB, Zhou H, Ding X, Zhang R. 2011. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B. 42(2):513–529.
- Huang GB, Zhu QY, Siew CK, Wang W, Liu X. 2006. Extreme learning machine: theory and applications. Neurocomputing [Internet]. 70(1–3):489–501. doi:10.1016/j.neucom.2016.06.079.
- Huang J, Zhu Q, Yang L, Feng J. 2016. A non-parameter outlier detection algorithm based on natural neighbor. Know Based Syst. 92:71–77.
- Huchet M, Babarit A, Ducrozet G, Gilloteaux JC, Ferrant P. 2021. Nonlinear deterministic sea wave prediction using instantaneous velocity profiles. Ocean Eng. 220.
- Jain P, Deo MC, Latha G, Rajendran V. 2011. Real time wave forecasting using wind time history and numerical model. Ocean Model. 36(1–2):26–39.
- Kim SU, Kim G, Jeong WM, Jun K. 2013. Uncertainty analysis on extreme value analysis of significant wave height at eastern coast of Korea. Appl Ocean Res. 41:19–27.
- Lavidas G, Venugopal V. 2018. Application of numerical wave models at European coastlines: a review. Renew Sustain Energy Rev. 92:489–500.
- Lima AR, Cannon AJ, Hsieh WW. 2015. Nonlinear regression in environmental sciences using extreme learning machines: a comparative evaluation. Environ Model Softw. 73:175–188.
- Liu N, Wang H. 2010. Ensemble based extreme learning machine. IEEE Signal Process Lett. 17(8):754–757.
- Mahmoodi K, Ghassemi H. 2018. Outlier detection in ocean wave measurements by using unsupervised data mining methods. Polish Marit Res. 25(1):44–50.
- Mahmoodi K, Ghassemi H, Nowruzi H. 2017. Data mining models to predict ocean wave energy flux in the absence of wave records. Sci J Marit Univ Szczecin Zeszyty Nauk Akad Morskiej W Szczecinie. 49(121):119–129.
- Mahmoodi K, Ghassemi H, Nowruzi H, Shora MM. 2019a. Prediction of the hydrodynamic performance and cavitation volume of the marine propeller using gene expression programming. Ships Offshore Struct. 14:7.
- Mahmoodi K, Ghassemi H, Razminia A. 2019b. Temporal and spatial characteristics of wave energy in the Persian Gulf based on the ERA5 reanalysis dataset. Energy. 187.
- Mahmoodi K, Ghassemi H, Razminia A. 2020. Wind energy potential assessment in the Persian Gulf: a spatial and temporal analysis. Ocean Eng [Internet]. 216:107674. https://linkinghub.elsevier.com/retrieve/pii/S0029801820306685.
- Meng X, Zhang P, Xu Y, Xie H. 2020. Construction of decision tree based on C4.5 algorithm for online voltage stability assessment. Int J Electr Power Energy Syst. 118.
- Muraleedharan G, Lucas C, Guedes Soares C, Unnikrishnan Nair N, Kurup PG. 2012. Modelling significant wave height distributions with quantile functions for estimation of extreme wave heights. Ocean Eng. 54:119–131.
- Nowruzi H, Ghassemi H, Ghiasi M. 2017. Performance predicting of 2D and 3D submerged hydrofoils using CFD and ANNs. J Mar Sci Technol. 22(4):710–733.
- Oikonomou CLG, Gradowski M, Kalogeri C, Sarmento AJNA. 2020. On defining storm intervals: extreme wave analysis using extremal index inferencing of the run length parameter. Ocean Eng. 217.
- Ozbahceci BO. 2020. Extreme value statistics of wind speed and wave height of the Marmara Sea based on combined radar altimeter data. Adv Sp Res. 66(10):2302–2318.
- Powers DMW. 2011. Evaluation: from precision, recall and F-measure to roc, informedness. Marked Corr J Mach Learn Technol. 2(1):37–63.
- Robles-Velasco A, Cortés P, Muñuzuri J, Onieva L. 2020. Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliab Eng Syst Saf. 196.
- Vaghefi M, Mahmoodi K, Akbari M. 2018. A comparison among data mining algorithms for outlier detection using flow pattern experiments. Sci Iran. 0(0):0–0.
- Vaghefi M, Mahmoodi K, Akbari M. 2019a. Detection of outlier in 3D flow velocity collection in an open-channel bend using various data mining techniques. Iran J Sci Technol Trans Civ Eng [Internet]. 43(2):197–214. [accessed 2019 May 16]. http://link.springer.com/10.1007/s40996-018-0131-2.
- Vaghefi M, Mahmoodi K, Akbari M. 2019b. Detection of outlier in 3D flow velocity collection in an open-channel bend using various data mining techniques. Iran J Sci Technol Trans Civ Eng. 43(2):197–214.
- Vaghefi M, Mahmoodi K, Setayeshi S, Akbari M. 2020. Application of artificial neural networks to predict flow velocity in a 180° sharp bend with and without a spur dike. Soft Comput. 24:12.
- Vanem E. 2020. Bivariate regional extreme value analysis for significant wave height and wave period. Appl Ocean Res. 101.
- Windt C, Davidson J, Ringwood J V. 2018. High-fidelity numerical modelling of ocean wave energy systems: a review of computational fluid dynamics-based numerical wave tanks. Renew Sustain Energy Rev. 93:610–630.
- Zanaty EA. 2012. Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egypt Inform J. 13(3):177–183.