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A hierarchical selective ensemble randomized neural network hybridized with heuristic feature selection for estimation of sea-ice thickness

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Abstract

In this paper, a hybrid intelligent system is developed to estimate sea-ice thickness along the Labrador coast of Canada. The developed intelligent system consists of two main parts. The first part is a heuristic feature selection algorithm used for processing a database to select the most effective features. The second part is a hierarchical selective ensemble randomized neural network (HSE-RNN) that is used to create a nonlinear map between the selected features and sea-ice thickness. The required data for processing have been collected from two sensors, i.e. moderate resolution imaging spectro-radiometer (MODIS), and advanced microwave scanning radiometer-earth (AMSR-E) observing system. To evaluate the computational advantages of the proposed intelligent framework, it is given brightness temperatures data captured at two different frequencies (low frequency, 6.9GHz, and high frequency, 36.5GHz) in addition to both atmospheric and oceanic variables from forecasting models. The obtained results demonstrate the computational power of the developed intelligent algorithm for the estimation of sea-ice thickness along the Labrador coast.

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Authors and Affiliations

  1. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

    K. Andrea Scott & Nasser L. Azad

  2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada

    Ahmad Mozaffari & Shoja’eddin Chenouri

Authors
  1. Ahmad Mozaffari
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  2. K. Andrea Scott
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  3. Nasser L. Azad
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  4. Shoja’eddin Chenouri
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Correspondence to Shoja’eddin Chenouri.

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Mozaffari, A., Scott, K.A., Azad, N.L. et al. A hierarchical selective ensemble randomized neural network hybridized with heuristic feature selection for estimation of sea-ice thickness. Appl Intell 46, 16–33 (2017). https://doi.org/10.1007/s10489-016-0815-x

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