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| Bocco,Mónica; Willington,Enrique; Arias,Mónica. |
| The incident solar radiation on soil is an important variable used in agricultural applications; it is also relevant in hydrology, meteorology and soil physics, among others. To estimate this variable, empirical models have been developed using several parameters and, recently, prognostic and prediction models based on artificial intelligence techniques such as neural networks. The aim of this work was to develop linear models and neural networks, multilayer perceptron, to estimate daily global solar radiation and compare their efficiency in its application to a region of the Province of Salta, Argentina. Relative sunshine duration, maximum and minimum temperature, rainfall, binary rainfall and extraterrestrial solar radiation data for the period... |
| Tipo: Journal article |
Palavras-chave: Modeling; Prediction; Linear regression; Multilayer perceptron. |
| Ano: 2010 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392010000300010 |
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| Bocco,Mónica; Ovando,Gustavo; Sayago,Silvina. |
| The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Modelling; Prediction; Backpropagation neural networks. |
| Ano: 2006 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2006000200001 |
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| Bocco,Mónica; Sayago,Silvina; Tártara,Enzo. |
| En este trabajo se desarrollan modelos basados en los métodos de programación multicriterio: se utiliza la programación multiobjetivo, complementada con la programación compromiso y por metas, con el fin de evaluar la optimización de más de un objetivo económico. Estos modelos tienen como propósito predecir ex-ante los resultados económicos, maximización del margen bruto y minimización del riesgo empresarial, que se observarán en los sistemas hortícolas al adoptar nuevas alternativas de producción, contemplando distintas restricciones agroeconómicas. Se realizó su aplicación a las pequeñas explotaciones hortícolas del Cinturón Verde de Córdoba, Argentina, para explicar el cambio de la situación económica de la empresa al seleccionar y adoptar nuevos planes... |
| Tipo: Journal article |
Palavras-chave: Modelos multicriterio; Optimización; Adopción; Productos hortícolas. |
| Ano: 2002 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0365-28072002000300010 |
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| Bocco,Mónica; Ovando,Gustavo; Sayago,Silvina; Willington,Enrique. |
| Land cover data represent environmental information for a variety of scientific and policy applications, so its classification from satellite images is important. Since neural networks (NN) do not require a hypothesis about data distribution, they are valuable tools to classify satellite images. The objectives of this work were to develop NN models to classify land cover data from information from satellite images and to evaluate them when different input variables are used. MODIS-MYD13Q1 satellite images and data of 85 plots in Córdoba, Argentina, were used. Five NN models of multi-layer feed-forward perceptron were designed. Four of these received NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), red (RED) and near infrared... |
| Tipo: Journal article |
Palavras-chave: Modeling; Back-propagation neural networks; Remote sensing; Crops-bare soil. |
| Ano: 2007 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0365-28072007000400009 |
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| Ovando,Gustavo; Bocco,Mónica; Sayago,Silvina. |
| En este trabajo se desarrollaron modelos basados en redes neuronales del tipo "backpropagation", para predecir la ocurrencia de heladas, a partir de datos meteorológicos de temperatura, humedad relativa, nubosidad, dirección y velocidad del viento. El entrenamiento y la validación de las redes se realizaron utilizando 24 años de datos meteorológicos correspondientes a la estación de Río Cuarto, Córdoba, Argentina, separados en 10 años como conjunto de datos de entrenamiento y 14 como conjunto de datos de validación. Se construyeron diferentes modelos para evaluar el comportamiento de las redes cuando se usan distintos números de variables de entrada y/o neuronas en la capa oculta y las probabilidades de aciertos en los resultados de predicción para los... |
| Tipo: Journal article |
Palavras-chave: Redes neuronales; Predicción de heladas; Modelos; Backpropagation. |
| Ano: 2005 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0365-28072005000100007 |
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