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Wave Height Prediction Using Artificial Immune Recognition Systems (AIRS) and Some Other Data Mining Techniques

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

Significant wave height (SWH) prediction is an important task in coastal engineering activities. Due to difficulties and complexities in predicting SWH using traditional numerical and empirical models, in the past decades, a high tendency to use soft computing approaches has been observed. In this paper, an artificial immune recognition system (AIRS), which is a recently developed data mining approach, is utilized for SWH prediction. The results of AIRS model are compared with those of artificial neural networks (ANN), regression tree induction (named M5P), Rough Set Theory, Bayesian networks and support vector regression models. The AIRS has been successfully employed in some engineering applications, but its efficiency and applicability for SWH prediction have not been investigated. In this paper, the AIRS model is utilized for the SWH prediction in the Lake Superior in North America. Comparing the results of this model and five other data mining techniques shows that the AIRS and ANN can outperform other models.

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Authors and Affiliations

  1. School of Engineering, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran

    Mohammad Reza Nikoo

  2. School of Civil Engineering and Center of Excellence for Engineering and Management of Civil Infrastructures, University of Tehran, College of Engineering, Tehran, Iran

    Reza Kerachian

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  1. Mohammad Reza Nikoo
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  2. Reza Kerachian
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Correspondence to Mohammad Reza Nikoo.

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Nikoo, M.R., Kerachian, R. Wave Height Prediction Using Artificial Immune Recognition Systems (AIRS) and Some Other Data Mining Techniques. Iran J Sci Technol Trans Civ Eng 41, 329–344 (2017). https://doi.org/10.1007/s40996-017-0067-y

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  • DOI: https://doi.org/10.1007/s40996-017-0067-y

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