Resumo

RAMIREZ-FERNANDEZ, Salomón Einstein  e  LIZARAZO-SALCEDO, Iván Alberto. classification of cloud masses from weather imagery using machine learning algorithms. Rev.fac.ing.univ. Antioquia [online]. 2014, n.73, pp.43-57. ISSN 0120-6230.

Accurate identification of precipitating clouds is a challenging task. In the present work, Support Vector Machines, Decisión Trees and Random Forests algorithms were applied to discrimínate between precipitating clouds and non-precipitating clouds from a satellite weather image GOES- 13 covering the Colombian territory. The objective of this study was to evaluate the performance of machine learning (ML) algorithms for digital classification of cloud masses in terms of thematic accuracy classification using the conventional Mahalanobis algorithm as benchmark. Results show that ML algorithms provide more accurate classification of cloud masses than conventional algorithms. The best accuracy was obtained using Random Forests (RF), with an overall thematic accuracy of 97%. Furthermore, the classification obtained with the RF algorithm was compared pixel-to-pixel with NASA Tropical Rainfall Measurement Mission (TRMM) rainfall estimates, obtaining an overall accuracy of 94%. ML algorithms can therefore be used to improve current precipitating clouds identification methods.

Palavras-chave : cloud mass classification; machine learning algorithms; weather images; decision trees; support vector machines; random forests.

        · resumo em Espanhol     · texto em Espanhol     · Espanhol ( pdf )