Estruturação de uma base de dados pluviométricos para a bacia hidrográfica do rio Parnaíba a partir de dados de sensoriamento remoto

Morais, Reurysson Chagas de Sousa

doi:10.29327/15210.1-3

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the transition areas, behaviors similar to those obtained here were achieved by Moraes et al [ 57 ] for the complexity maps for the He/Hmax and SDL measurements in a region with conditions similar to those studied here (a conservation unit surrounded by transition areas of different land uses), showing the coherence of the results generated by these measures. This also happened in a study by Mattos et al [ 52 ] for the same area researched by Moraes et al [ 57 ] using the CompPlex Janus, which observed behaviors similar to those reported here in relation to the window size and the CBERS satellite band used, suggesting that both the identification of borders and transition areas and the distinction of vegetation uses are consistent marks of the complexity measures present in CompPlex Janus.…”

Section: Resultssupporting

confidence: 88%

Metrics based on information entropy applied to evaluate complexity of landscape patterns

et al. 2022

View full text Add to dashboard Cite

Landscape is an ecological category represented by a complex system formed by interactions between society and nature. Spatial patterns of different land uses present in a landscape reveal past and present processes responsible for its dynamics and organisation. Measuring the complexity of these patterns (in the sense of their spatial heterogeneity) allows us to evaluate the integrity and resilience of these complex environmental systems. Here, we show how landscape metrics based on information entropy can be applied to evaluate the complexity (in the sense of spatial heterogeneity) of patches patterns, as well as their transition zones, present in a Cerrado conservation area and its surroundings, located in south-eastern Brazil. The analysis in this study aimed to elucidate how changes in land use and the consequent fragmentation affect the complexity of the landscape. The scripts CompPlex HeROI and CompPlex Janus were created to allow calculation of information entropy (He), variability (He/Hmax), and López-Ruiz, Mancini, and Calbet (LMC) and Shiner, Davison, and Landsberg (SDL) measures. CompPlex HeROI enabled the calculation of these measures for different regions of interest (ROIs) selected in a satellite image of the study area, followed by comparison of the complexity of their patterns, in addition to enabling the generation of complexity signatures for each ROI. CompPlex Janus made it possible to spatialise the results for these four measures in landscape complexity maps. As expected, both for the complexity patterns evaluated by CompPlex HeROI and the complexity maps generated by CompPlex Janus, the areas with vegetation located in a region of intermediate spatial heterogeneity had lower values for the He and He/Hmax measures and higher values for the LMC and SDL measurements. So, these landscape metrics were able to capture the behaviour of the patterns of different types of land use present in the study area, bringing together uses linked to vegetation with increased canopy coverage and differentiating them from urban areas and transition areas that mix different uses. Thus, the algorithms implemented in these scripts were demonstrated to be robust and capable of measuring the variability in information levels from the landscape, not only in terms of spatial datasets but also spectrally. The automation of measurement calculations, owing to informational entropy provided by these scripts, allows a quick assessment of the complexity of patterns present in a landscape, and thus, generates indicators of landscape integrity and resilience.

show abstract

Section: Resultssupporting

confidence: 88%

Metrics based on information entropy applied to evaluate complexity of landscape patterns

et al. 2022

View full text Add to dashboard Cite

show abstract

Análise do consumo de energia elétrica residencial utilizando machine learning

Moraes,

Hilasaca,

Belizario

et al. 2023

Revista Científica Multidisciplinar Núcleo do Conhecimento

View full text Add to dashboard Cite

O consumo de energia elétrica é uma preocupação global devido ao seu uso de forma ineficiente e descontrolado, resultando em gastos excessivos. Nesse contexto, o presente artigo propõe a aplicação de técnicas de inteligência artificial, em particular o machine learning, para analisar o consumo de energia residencial. Foram utilizados dados obtidos por meio de sensores que passaram por um pré-processamento para eliminar instâncias com dados incompletos. Uma categorização do consumo de energia é apresentada, permitindo que a inteligência artificial implementada realize a classificação correspondente. Também foi realizada uma análise de técnicas para dados desbalanceados referente ao consumo de energia, assim, de acordo com a quantidade de experimentos executados, são fornecidas recomendações para balanceamento com a menor perda de informação possível. A validação da metodologia foi feita comparando os rótulos verdadeiros com as categorias preditas. Os resultados demonstraram a eficácia da proposta na classificação, alcançando 98% de acurácia. Além disso, as visualizações por meio de UMAP, t-SNE e Coordenadas Paralelas destacaram a separação das instâncias de distintas categorias, indicando a eficácia da metodologia.

show abstract

Estruturação de uma base de dados pluviométricos para a bacia hidrográfica do rio Parnaíba a partir de dados de sensoriamento remoto

Cited by 2 publications

References 0 publications

Metrics based on information entropy applied to evaluate complexity of landscape patterns

Metrics based on information entropy applied to evaluate complexity of landscape patterns

Análise do consumo de energia elétrica residencial utilizando machine learning

Contact Info

Product

Resources

About