Cloud Classification Using Convolutional Neural Networks

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dc.contributor.advisor Galván, Inés M.
dc.contributor.advisor Aler, Ricardo
dc.contributor.author García Fernández, Antonio
dc.date.accessioned 2020-04-27T11:29:49Z
dc.date.available 2020-04-27T11:29:49Z
dc.date.issued 2019-09-23
dc.date.submitted 2019-10-08
dc.identifier.uri http://hdl.handle.net/10016/30210
dc.description.abstract The aim of this thesis is to analyze the performance of Convolutional Neural Networks (CNN) in the domain of automatic cloud classification. The study makes use of a set of images never before used on CNNs that was provided by the MATRAS (Modelización de la ATmósfera y RAdiación Solar) group [1] of the University of Jaén. The dataset is formed by a total of 1290 of ground-based pictures of the sky taken from Seville (Spain). In this dataset, cloud types are not represented equally in terms of the number of instances; it is unbalanced. Two datasets are created from the original dataset: a simple dataset with the two most prominent cloud types and a complex dataset with 6 cloud types. The types of cloud considered are stratocumulus and cirrus in the first case and, stratucumulus, cirrus, stratus, altocumulos, cumulus and clear sky in the second case. A total of 20 experiments are presented, activating or deactivating a presubsampling layer (with average pooling) placed before the first convolutional layer. The experiments also introduce the use of the adagrad optimizer instead of the default SGD (Stochastic Gradient Descent) optimizer. The resulting models for the 2 cloud type problem provide an accuracy rate of 91%, showing a positive response towards the presubsampling layer and the change in the optimizer. As for the complex problem, accuracy ranges from 68% to 76% depending on the model. In this domain, the presubsampling layer and adagrad optimizer proved to be better for the overall accuracy and the classification of stratocumulus while being detrimental to the classification of clear sky. Finally, in the 6 cloud types domain, some limitations were found as the difference in number of instances introduced a bias in some of the resulting models.
dc.language.iso eng
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Convolutional Neural Networks (CNN)
dc.subject.other Automatic Cloud Classification
dc.subject.other Solar energy
dc.title Cloud Classification Using Convolutional Neural Networks
dc.type bachelorThesis
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.description.degree Ingeniería Informática
dc.contributor.departamento Universidad Carlos III de Madrid. Departamento de Informática
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