Using Machine Learning Techniques to Recover Prismatic Cirrus Ice Crystal Size from 2-Dimensional Light Scattering Patterns

  • Daniel Priori
  • Giseli de Sousa
  • Mauro Roisenberg
  • Christopher Stopford
  • Evelyn Hesse
  • Emmanuel Salawu
  • Neil Davey
  • Yi Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)

Abstract

In this paper, we present a prediction model developed to identify particles size of ice crystals in clouds. The proposed model combines a Feed Forward Multi-Layer Perceptron neural network with Bayesian regularization backpropagation and other machine learning techniques for feature reduction with Principal Component Analysis and rotation invariance with Fast Fourier Transform. The proposed solution is capable of predicting the particle sizes with normalized mean squared error around 0.007. However, the proposed network model is not able to predict the size of very small particles (between 3 and 10 \({\upmu }\)m size) with the same precision as for the larger particles. Therefore, in this work we also discuss some possible reasons for this problem and suggest future points that need to be analysed.

Keywords

2d light scattering pattern Atmospheric particle Size prediction Fast Fourier Transform Neural network regression 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Daniel Priori
    • 1
  • Giseli de Sousa
    • 1
    • 2
  • Mauro Roisenberg
    • 1
  • Christopher Stopford
    • 2
  • Evelyn Hesse
    • 2
  • Emmanuel Salawu
    • 3
  • Neil Davey
    • 2
  • Yi Sun
    • 2
  1. 1.Connectionism and Cognitive Science Lab, Deparment of Informatic and StatisticFederal University of Santa CatarinaFlorianopolisBrazil
  2. 2.Science and Technology Research InstituteUniversity of HertfordshireHatfieldUK
  3. 3.TIGP Bioinformatics Program, Academia SinicaTaipeiTaiwan

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