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Classification of Low-Level Atmospheric Structures Based on a Pyramid Representation and a Machine Learning Method

  • Sebastián Sierra
  • Juan F. Molina
  • Angel Cruz-Roa
  • José Daniel Pabón
  • Raúl Ramos-Pollán
  • Fabio A. González
  • Hugo Franco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

The atmosphere is a highly complex fluid system where multiple intrinsic and extrinsic phenomena superpose at same spatial and temporal dominions and different scales, making its characterization a challenging task. Despite the novel methods for pattern recognition and detection available in the literature, most of climate data analysis and weather forecast rely on the ability of specialized personnel to visually detect atmospheric patterns present in climate data plots. This paper presents a method for classifying low-level wind flow configurations, namely: confluences, difluences, vortices and saddle points. The method combines specialized image features to capture the particular structure of low-level wind flow configurations through a pyramid layout representation and a state-of-the-art machine learning classification method. The method was validated on a set of volumes extracted from climate simulations and manually annotated by experts. The best results into the independent test dataset was 0.81 of average accuracy among the four atmospheric structures.

Keywords

Support Vector Machine Saddle Point Random Forest Convolutional Neural Network Radial Basis Function Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sebastián Sierra
    • 2
  • Juan F. Molina
    • 1
  • Angel Cruz-Roa
    • 2
  • José Daniel Pabón
    • 1
    • 2
    • 3
  • Raúl Ramos-Pollán
    • 3
  • Fabio A. González
    • 2
  • Hugo Franco
    • 1
  1. 1.Computer Engineering DepartmentUniversidad CentralBogotá D.C.Colombia
  2. 2.MindLab Research GroupUniversidad Nacional de ColombiaBogotáColombia
  3. 3.Centre for Supercomputing and Scientific ComputingUniversidad Industrial de SantanderBucaramangaColombia

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