Navegando por Assunto "Reconhecimento de padrões"

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Acesso aberto (Open Access)
Clusterização de padrões espaço-temporais de precipitação na Amazônia via deep convolutional autoencoder
(Universidade Federal do Pará, 2023-07-07) SILVA, Vander Augusto Oliveira da; TEIXEIRA, Raphael Barros; http://lattes.cnpq.br/4902824086591521; https://orcid.org/0000-0003-2993-802X
Studies using different machine learning methods for knowledge discovery and pattern recognition in precipitation time series are increasingly frequent in the literature. Identify and analyze patterns in precipitation time series in a particular region is fundamental for its socioeconomic development. Therefore, it can be stated that knowledge and understanding of the rainfall characteristics of the regions are important to enable the planning of the use, management and conservation of water resources. The natural phenomenon of precipitation is a fundamental process with a direct impact on watersheds and on human and environmental development. The variability of this phenomenon has important implications for the navigability of rivers, individual abundance and species richness. In recent years, many studies with this approach have been carried out in Brazil, mainly in the Amazon region. This research aimed to develop a computational method for analyzing time series of precipitation using machine learning techniques with unsupervised learning, in order to propose an method capable of extracting complex features from the data, obtaining a map of attributes at low dimensionality for pattern recognition, discovery of homogeneous regions with respect to precipitation and approximate reconstruction of precipitation time series in the Legal Amazon. The proposed deep learning neural network model is trained to learn the main and most complex features of the original data and present them in low dimensionality in latent space. After the training, the results are promising, the observations of the reconstructed data showed a good performance as evaluated by the RMSE and NRMSE metric with resulting values equal to 0.06610 and 0.3355 respectively. The analysis of the representation of the data in low dimension was applied and analyzed by a clustering structure using hierarchical agglomerative with Ward’s method. This methodology also showed good results, as it carried out consistent groupings characterizing ho- mogeneous regions in relation to precipitation data. Thus, demonstrating that the representation in low dimensionality carried the main characteristics of the time series of the analyzed data. It is noteworthy that the method developed in this study can be applied not only in the Amazon region, but also in other areas with similar challenges related to time series analysis.
Acesso aberto (Open Access)
Metodologia integrada utilizando sensoriamento remoto em redes neurais artificiais na quantificação do potencial de biomassa florestal na Amazônia
(Universidade Federal do Pará, 2008-04-08) ALMEIDA, Arthur da Costa; ROCHA, Brigida Ramati Pereira da; http://lattes.cnpq.br/9943372249006341
Pattern recognition and pattern classification in digital images is a very important skill, today. With them, it is possible to recognize and identify target objects in those images. This work proposes an integrated methodology for pattern recognition related to biomass in the Amazon tropical rainforest to extract information about bioenergetics potential for electric energy production for use with isolated Amazonian communities. To achieve this aim, information gathered about forest inventory was mixed with pattern classification and recognition in medium resolution satellite imagery such as those from LANDSAT and CBERS. The approach used in this work comes from the computational intelligence area, using artificial neural networks equipped with radial basis functions and Kohonen´s self organizing maps. The results serve as input to a geographical information system application which creates and manages a geographical database for energetic planning with renewable energy resources applicable to isolated Amazonian communities.