estimativa de ocorrência de precipitação em áreas agrícolas utilizando floresta de caminhos ótimos agricultural areas precipitation occurrence estimation using optimum path forest
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2010
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Abstract
As condições meteorológicas são determinantes para a produção agrícola; a precipitação, em particular, pode ser citada como a mais influente por sua relação direta com o balanço hídrico. Neste sentido, modelos agrometeorológicos, os quais se baseiam nas respostas das culturas às condições meteorológicas, vêm sendo cada vez mais utilizados para a estimativa de rendimentos agrícolas. Devido às dificuldades de obtenção de dados para abastecer tais modelos, métodos de estimativa de precipitação utilizando imagens dos canais espectrais dos satélites meteorológicos têm sido empregados para esta finalidade. O presente trabalho tem por objetivo utilizar o classificador de padrões "floresta de caminhos ótimos" para correlacionar informações disponíveis no canal espectral infravermelho do satélite meteorológico GOES-12 com a refletividade obtida pelo radar do IPMET/UNESP localizado no município de Bauru, visando o desenvolvimento de um modelo para a detecção de ocorrência de precipitação. Nos experimentos foram comparados quatro algoritmos de classificação: redes neurais artificiais (ANN), k-vizinhos mais próximos (k-NN), máquinas de vetores de suporte (SVM) e floresta de caminhos ótimos (OPF). Este último obteve melhor resultado, tanto em eficiência quanto em precisão.
Meteorological conditions are determinant for the agricultural production; in particular, rainfall may be cited as the most important because having direct relation with water balance. To estimate agricultural production, agrometeorological models based on the cultures behavior under meteorological conditions, have been used. Since it is difficult to obtain the required data to these models, rainfall estimation techniques using meteorological satellites images from spectral channels have been used. The objective of the present work is to apply the Optimum-Path Forest pattern classifier to the agrometeorological research field in order to correlate the available information from GOES-12 satellite infrared spectral channel images, to the reflectivity data obtained by the IPMET/UNESP radar located at Bauru, aiming to develop a model for precipitation occurrence identification. In the experiments we compared four classification algorithms: Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Support vector Machines (SVM) and optimum-Path Forest (OPF). this last one shows the best results in terms of accuracy rate and running time.
Meteorological conditions are determinant for the agricultural production; in particular, rainfall may be cited as the most important because having direct relation with water balance. To estimate agricultural production, agrometeorological models based on the cultures behavior under meteorological conditions, have been used. Since it is difficult to obtain the required data to these models, rainfall estimation techniques using meteorological satellites images from spectral channels have been used. The objective of the present work is to apply the Optimum-Path Forest pattern classifier to the agrometeorological research field in order to correlate the available information from GOES-12 satellite infrared spectral channel images, to the reflectivity data obtained by the IPMET/UNESP radar located at Bauru, aiming to develop a model for precipitation occurrence identification. In the experiments we compared four classification algorithms: Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Support vector Machines (SVM) and optimum-Path Forest (OPF). this last one shows the best results in terms of accuracy rate and running time.
| Reference Key |
freitas2010revistaestimativa
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| Authors | ;Greice Martins de Freitas;João Paulo Papa;Ana Maria Heuminski de Avila;Alexandre Xavier Falcão Hilton Silveira Pinto;Hilton Silveira Pinto |
| Journal | Applied microbiology and biotechnology |
| Year | 2010 |
| DOI |
10.1590/S0102-77862010000100002
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