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

        Author
        Priori,, Daniel
        de Sousa, Giseli
        Roisenberg, Mauro
        Stopford, Chris
        Hesse, Evelyn
        Davey, Neil
        Sun, Yi
        Attention
        2299/20613
        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 withBayesian regularization backpropagation and other machine learning techniques for feature reduction with Principal Component Analysis androtation 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 topredict the size of very small particles (between 3 and 10 µm 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.
        Publication date
        2016-08-13
        Published in
        Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
        Published version
        https://doi.org/10.1007/978-3-319-44781-0_44
        Other links
        http://hdl.handle.net/2299/20613
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