Consulta: subjectFacets:"Self-organizing maps"
Registros recuperados: 4
Data/hora: 26/07/2024 22:55:25

Artificial neural network for ecological-economic zoning as a tool for spatial planning
Provedor de dados: Pesquisa Agropecuária Brasileira
Autores: Sadeck,Luis Waldyr Rodrigues; Lima,Aline Maria Meiguins de; Adami,Marcos.
Abstract: The objective of this work was to analyze social and environmental information through an artificial neural network-self-organizing map (ANN-SOM), in order to provide subsidy to ecological-economic zoning (EEZ) as a tool to reduce the subjectivity of the process. The study area comprises 16 municipalities in the northeast of the state of Pará, Brazil, representative of the agricultural development in the state. Data processing involved three steps: preparation of the data in a geographic information system (GIS) environment; mathematical processing (ANN-SOM) of the data; and visualization and interpretation of the processing results, allowing the spatial planning of northeastern Pará. The results comprised 13 classes, regrouped according to...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Amazon; Regional planning; Regionalization; Self-organizing maps.
Ano: 2017
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2017001101050

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach
Provedor de dados: Institutional Archive of Ifreme (ArchiMer)
Autores: Renosh, Pannimpullath R.; Jourdin, Frederic; Charantonis, Anastase A.; Yala, Khalil; Rivier, Aurelie; Badran, Fouad; Thiria, Sylvie; Guillou, Nicolas; Leckler, Fabien; Gohin, Francis; Garlan, Thierry.
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden...
Tipo: Text
Palavras-chave: Suspended particulate inorganic matter; Self-organizing maps; Hidden Markov Model; Machine learning; English Channel; ROMS.
Ano: 2017
URL: http://archimer.ifremer.fr/doc/00415/52653/53511.pdf

Estudo da função Gama e mapa auto-organizável como metodologia para validação e imputação de precipitação diária em séries temporais de estações meteorológicas.
Provedor de dados: Informação Tecnológica em Agricultura (Infoteca-e)
Autores: MANCINI, A. L.; VICTORIA, D. de C.
Resumo - O uso da função Gama e de mapas auto-organizáveis são métodos descritos na literatura para compreensão do regime pluviométrico de uma região. Este trabalho relata o estudo destas ferramentas para validação e imputação de precipitação diária em séries históricas diárias de 18 estações meteorológicas em localidades diversas do Brasil. A função Gama não se mostrou eficaz para este propósito e o mapa auto-organizável teve resultados muito próximos comparados com o uso da média da precipitação. Mas considerando que é possível alterar o tamanho do mapa e incluir variações no vetor de entrada dos dados, novos estudos são necessários para verificar se é possível melhorar os resultados.
Tipo: Folhetos
Palavras-chave: Métodos estatísticos; Agrupamento de dados; Precipitação; Validação de precipitação; Função Gama; Mapas auto-organizáveis; Precipitation validation; Statistical methods; Data clustering; Gamma function; Self-organizing maps.
Ano: 2023
URL: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1163381

Phytoplankton Diversity in the Mediterranean Sea From Satellite Data Using Self-Organizing Maps
Provedor de dados: Institutional Archive of Ifreme (ArchiMer)
Autores: El Hourany, Roy; Abboud-abi Saab, Marie; Faour, Ghaleb; Mejia, Carlos; Crepon, Michel; Thiria, Sylvie.
We present a new method to identify phytoplankton functional types (PFTs) in the Mediterranean Sea from ocean color data (GlobColour data in the present study) and AVHRR sea surface temperature. The principle of the method is constituted by two very fine clustering algorithms, one mapping the relationship between the satellite data and the pigments and the other between the pigments and the PFTs. The clustering algorithms are constituted of two efficient self-organizing maps, which are neural network classifiers. We were able to identify and estimate the percentage of six PFTs: haptophytes, chlorophytes, cryptophytes, Synechococcus, Prochlorococcus, and diatoms. We found that these PFTs present a peculiar variability due to the complex physical and...
Tipo: Text
Palavras-chave: Phytoplankton; Secondary phytoplankton pigments; Self-organizing maps; Classification; Mediterranean Sea; Remote sensing.
Ano: 2019
URL: https://archimer.ifremer.fr/doc/00589/70145/68135.pdf