I. Pulido-Calvo - Academia.edu (original) (raw)
Papers by I. Pulido-Calvo
Implemento mecánico para cuchara bivalva en la ejecución de pozos verticales de gran diámetro. La... more Implemento mecánico para cuchara bivalva en la ejecución de pozos verticales de gran diámetro. La presente invención tiene como objeto la mejora de un procedimiento para ejecutar pozos verticales subterráneos de gran diámetro mediante cuchara bivalva de acero. La invención introduce mejoras en la excavación de pozos verticales, realizados mediante cuchara bivalva de acero, cuando se llega a capas de arcilla o limos que impiden que el sistema de cerramiento del pozo. La invención consiste en un implemento de acero que se fija a La cuchara bivalva mediante tornillos, colocando un implemento a un lado de la cuchara bivalva y otro implemento en el otro lado de dicha cuchara, de forma que cuando la bivalva se abre, estos implementos sobresalen del ancho de la excavación, rompiendo estos dispositivos la capa de arcilla o limos y facilitando, de este modo, el descenso seguro de los tubos de hormigón.Peer reviewedUniversidad de Almería, Universidad de Huelva, Consejo Superior de Investigaci...
Water, 2021
One of the unit operations involved in the production of olive oil is the separation of liquid–li... more One of the unit operations involved in the production of olive oil is the separation of liquid–liquid systems (and other multiphase flows) in their fundamental phases. The use of helical separators could be an alternative to be considered for that task in order to reduce energy consumption and improve the quality of products in olive oil mills (‘almazaras’). In this work, four models of helical separators have been built and tested in order to manage olive oil and water two-phase flows (with olive oil as the majority phase). Separation yields were analyzed from a dimensional analysis perspective, considering variables such as density and viscosity, flow rate, head losses, or the water concentration in the flows studied. The best separation yields (of the order of 80% to 100%) were obtained for olive oil–water two-phase flows in which the water concentrations could be higher, in some cases, than 5–10% for Reynolds numbers of below 60.
En este trabajo se analiza el efecto que la liberalización del mercado eléctrico tiene sobre la v... more En este trabajo se analiza el efecto que la liberalización del mercado eléctrico tiene sobre la variación de los regímenes de temperatura del agua en plantas de acuicultura intensiva que aprovechan los efluentes de refrigeración de centrales generadoras de electricidad. Para ello se han utilizado datos de una instalación dedicada al engorde de anguilas europeas, la cual toma el agua caliente del efluente de refrigeración de la Central Térmica de Puente Nuevo (Córdoba). Los resultados indican que la liberalización del mercado del sector eléctrico tiene una influencia significativa sobre la forma y cantidad de energía generada por la Central Térmica, y por consiguiente sobre el régimen termal del efluente de refrigeración. Los niveles de temperatura en el interior de la instalación son dependientes asimismo de la temperatura del agua en el efluente de refrigeración, estimándose la disminución de los índices de crecimiento debidos a este factor en un 5%.
En este estudio se analiza el crecimiento diario de dos alcornoques situados en una parcela de Hi... more En este estudio se analiza el crecimiento diario de dos alcornoques situados en una parcela de Hinojos (Huelva) en el ano 2004. Los datos de crecimiento se adquirieron cada 15 minutos mediante galgas extensiometricas que se situaron sobre y bajo corcho de cada uno de los individuos. Las variables climaticas y edaficas que intervienen se recogieron cada 15 minutos mediante dispositivos instalados en la parcela. Para cada dia se calculo la maxima contraccion diaria, la maxima dilatacion diaria y el crecimiento diario y se calcularon los valores medios, maximos y minimos de las variables edaficas y climaticas para esos periodos. Con estos se pretendio la caracterizacion de los parametros de crecimiento de los dos alcornoques seleccionados mediante el uso de regresiones multiples y de redes neuronales computacionales, con los datos edaficos y climaticos como variables independientes. Los mejores resultados se consiguieron con los modelos neuronales con correlaciones superiores a 0,6 y m...
Ingeniería del agua, 2007
Desde hace unos años, las redes neuronales computacionales están siendo una de las herramientas m... more Desde hace unos años, las redes neuronales computacionales están siendo una de las herramientas más prometedoras para la estimación de caudales en cuencas. La mayoría de los trabajos de la literatura utilizan para las predicciones, junto con los datos registrados de caudales, otras variables de entrada de carácter hidro-metorológico. En este estudio se analizó el funcionamiento de redes neuronales de retropropagación para la estimación de caudales diarios en cuencas portuguesas, considerando que sólo los datos de caudal de días previos están disponibles para la calibración de los modelos. Además de los modelos tradicionales de redes neuronales que tienen como variables de entrada los caudales en días previos, se realizó un procedimiento de convolución en las neuronas de la capa de entrada y se probó una metodología híbrida combinando redes neuronales computacionales y modelos ARIMA. Los modelos neuronales complementados con un proceso de convolución dieron las mejores estimaciones c...
XLII JORNADAS DE AUTOMÁTICA : LIBRO DE ACTAS
Este artículo describe una propuesta para modificar la arquitectura de control de un Vehículo Ope... more Este artículo describe una propuesta para modificar la arquitectura de control de un Vehículo Operado Remotamente (ROV) destinado a la toma de datos e inspección de granjas acuícolas. La propuesta se basa en incluir un nuevo módulo de Localización, encargado de posicionar distintos tipos de elementos que se encuentren alrededor del ROV y puedan ser de interés para la explotación de la instalación. El proceso de localización se basa en el uso de un sensor de ultrasonido en conjunción con un sistema de localización del ROV, cuya combinación posibilita el posicionamiento local y global de los elementos de interés. El artículo describe el método de localización desarrollado y presenta resultados experimentales que validan la propuesta.
Estuarine, Coastal and Shelf Science
Journal of Irrigation and Drainage Engineering, 2003
ABSTRACT Time should be considered in carrying out the design and management of demand irrigation... more ABSTRACT Time should be considered in carrying out the design and management of demand irrigation distribution systems. In this paper, a method to characterize the pumping flow in demand pressurized systems throughout the day and irrigation season is presented. This method considers the temporal evolution of water requirements during the irrigation season and water demand concentration in certain periods of the irrigation day due to different electrical energy charges. The model was established based on data from an actual water distribution network of an irrigation district in southern Spain. The results differed significantly from those obtained using approaches based on establishing a uniform working probability for the outlets of the water distribution network at all hours of the irrigation day, which underestimated the circulating flows or system capacity. The most probable pumping flow with uniform probability was 3.1 m3/s, a smaller value than those obtained in the off-peak and average energy tariff times (4 and 3.4 m3/s, respectively). The total energy head required at the booster pumping in each period of the irrigation season was simulated. 10,000 randomly chosen scenarios were simulated for each irrigation day and each energy tariff time. The heterogeneous vertical stratification between 50 and 103 m of the required piezometric head was obtained as a function of the demanded flow for the water distribution system. This paper includes a pump selection algorithm for recommending least cost or optimum pump combinations in the distribution network and to evaluate the system's energy cost. The pump recommendations show that the optimal solution could have saved 41% of the pumping cost of the Fuente Palmera irrigation district.
Journal of Irrigation and Drainage Engineering, 2003
One of the main problems in the management of large water supply and distribution systems is the ... more One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models ͑exponential smoothing and ARIMA models͒, and computational neural networks ͑CNNs͒ are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are ͑1͒ water demands from previous days; ͑2͒ climatic data from previous days ͑maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration͒; ͑3͒ crop data ͑surfaces and crop coefficients͒; and ͑4͒ water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.
Fisheries Research, 2009
In this study the performances of computational neural networks (CNNs), multiple linear regressio... more In this study the performances of computational neural networks (CNNs), multiple linear regressions (MLRs) and generalised additive models (GAMs) to predict Pacific sardine (Sardinops sagax) landings and to analyse their relationships with environmental factors in the north area off Chile were studied. For this purpose several local and global environmental variables and indexes (sea surface temperature, sea level and Ekman transport index in the Chilean coast and, sea surface temperature in the area Niño 3 + 4 and Niño 1 + 2, and the south oscillation index) were considered as inputs or independent variables. Additionally, several CNNs were calibrated and validated adding the anchovy (Engraulis ringens) landings in the same area as model inputs. The time lags of the variables considered were selected through analysis of the non-linear cross-correlation functions and an alternative form of sensitivity analysis based on the approach of the missing value problem. The analysis of error measures with validation data set showed that the best results were obtained when local and global variables were used separately and combined with anchovy landings. Globally, the best result was given by a CNN with 18 input variables (model CNN 6(II) which only considered global variables and anchovy landings) and 10 neurons in a hidden layer. For this configuration the explained variance was slightly higher to 86% which supposed a standard error of prediction of 7.66%. These results were significantly better than those obtained with MLRs and GAMs. The strong correlation between predicted and observed sardine landings suggests that CNNs captured the trend of the historical data. Also, the generalisation capacity together the sensitivity analysis allowed us to identify the variables with a high weight in the model and partially to interpret the statistical functional relationships between these environmental variables and sardine landings.
Fisheries Research, 2010
Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. None... more Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. Nonetheless, such techniques are limited by problems of species identification. Several methods and techniques have been used in addressing the problem of acoustic identification species. In this paper, schools of anchovy, common sardine, and jack mackerel were classified using support vector machines (SVMs) and two types of supervised artificial neural networks (multilayer perceptron, MLP; and probabilistic neural networks, PNNs) during acoustic surveys in south-central Chile. Classification was done using a set of descriptors for the schools extracted from the acoustic records. The problem was approached through two multi-class SVMs classifiers: one-species-against-one (1-vs-1) and one-species-against-the-Rest (1vs-R). Multi-class classifications showed that the MLP neural network and SVM approach performed better than the PNN. The classification rates averaged 79.4% with PNN and 89.5% with MLP and SVM.
Biosystems Engineering, 2007
Information regarding water demand is key to managing consumption in irrigation districts. Foreca... more Information regarding water demand is key to managing consumption in irrigation districts. Forecasting water demand is one of the main problems for designers and managers of water delivery systems. This paper evaluates the performance of linear multiple regressions and feed ...
... Sánchez, Antonio Gil, Juan Carlos Martos, Rafael Rodríguez, Mª del Mar Anguita, David Lozano,... more ... Sánchez, Antonio Gil, Juan Carlos Martos, Rafael Rodríguez, Mª del Mar Anguita, David Lozano, M arco Antonio Rodríguez, Sergio Luis Aguilar, Silvina Debelis, y ... Para la obtenci n de la funci n de demanda asociada al caudal ya la altura piezom trica en toda la campa a ...
Aquatic Sciences, 2000
In this paper, we propose an alternative method to predict the Gonadosomatic Index (GSI), based o... more In this paper, we propose an alternative method to predict the Gonadosomatic Index (GSI), based on a technique known as computational neural networks (CNNs), with two main objectives:
Computers and Electronics in Agriculture, 2014
Production functions (PFs) are practical tools for not only irrigation scheduling but also in eco... more Production functions (PFs) are practical tools for not only irrigation scheduling but also in economic analysis as a mathematical relationship between relative grain yield and factors like evapotranspiration, irrigation water and salinity. This study was carried out in the Mashhad region of Iran during cropping years 2010 and 2011 to evaluate the performances of two data mining methods, decision tree and neural network, for deriving PFs of spring wheat under simultaneous drought and salinity stress compared with four well known regression-based PFs. The four well known PFs were: Jensen-PF (Jensen, 1968), Minhas-PF , modified Stewart-PF , and Nairizi-PF . Heading and flowering were the most sensitive growth stages followed by the stem elongation and booting. Salinity stress also affected grain yield and therefore was an important parameter for deriving PFs. In general, all the PFs were in agreement concerning the sensitivity of spring wheat to water stress. The neural network-based PF performed the best with a root mean square error equal to 44.27 g m À2 while the decision tree-based PF ranked fourth out of six in terms of accuracy. The most important advantage of the neural network-based PF was the flexible number of input parameters.
Journal of Irrigation and Drainage Engineering, 2003
ABSTRACT Time should be considered in carrying out the design and management of demand irrigation... more ABSTRACT Time should be considered in carrying out the design and management of demand irrigation distribution systems. In this paper, a method to characterize the pumping flow in demand pressurized systems throughout the day and irrigation season is presented. This method considers the temporal evolution of water requirements during the irrigation season and water demand concentration in certain periods of the irrigation day due to different electrical energy charges. The model was established based on data from an actual water distribution network of an irrigation district in southern Spain. The results differed significantly from those obtained using approaches based on establishing a uniform working probability for the outlets of the water distribution network at all hours of the irrigation day, which underestimated the circulating flows or system capacity. The most probable pumping flow with uniform probability was 3.1 m3/s, a smaller value than those obtained in the off-peak and average energy tariff times (4 and 3.4 m3/s, respectively). The total energy head required at the booster pumping in each period of the irrigation season was simulated. 10,000 randomly chosen scenarios were simulated for each irrigation day and each energy tariff time. The heterogeneous vertical stratification between 50 and 103 m of the required piezometric head was obtained as a function of the demanded flow for the water distribution system. This paper includes a pump selection algorithm for recommending least cost or optimum pump combinations in the distribution network and to evaluate the system's energy cost. The pump recommendations show that the optimal solution could have saved 41% of the pumping cost of the Fuente Palmera irrigation district.
The age structure, growth and reproduction of Lepomis gibbosa (L. 1758) (= L. gibbosus) was studi... more The age structure, growth and reproduction of Lepomis gibbosa (L. 1758) (= L. gibbosus) was studied from March 1993 to September 1994 in a tributary stream of the Guadalquivir River. The maximum age observed was 5+ years both in males and females. In the O+ group, seasonal growth began in February and lasted 8 months. Males and females matured during
Implemento mecánico para cuchara bivalva en la ejecución de pozos verticales de gran diámetro. La... more Implemento mecánico para cuchara bivalva en la ejecución de pozos verticales de gran diámetro. La presente invención tiene como objeto la mejora de un procedimiento para ejecutar pozos verticales subterráneos de gran diámetro mediante cuchara bivalva de acero. La invención introduce mejoras en la excavación de pozos verticales, realizados mediante cuchara bivalva de acero, cuando se llega a capas de arcilla o limos que impiden que el sistema de cerramiento del pozo. La invención consiste en un implemento de acero que se fija a La cuchara bivalva mediante tornillos, colocando un implemento a un lado de la cuchara bivalva y otro implemento en el otro lado de dicha cuchara, de forma que cuando la bivalva se abre, estos implementos sobresalen del ancho de la excavación, rompiendo estos dispositivos la capa de arcilla o limos y facilitando, de este modo, el descenso seguro de los tubos de hormigón.Peer reviewedUniversidad de Almería, Universidad de Huelva, Consejo Superior de Investigaci...
Water, 2021
One of the unit operations involved in the production of olive oil is the separation of liquid–li... more One of the unit operations involved in the production of olive oil is the separation of liquid–liquid systems (and other multiphase flows) in their fundamental phases. The use of helical separators could be an alternative to be considered for that task in order to reduce energy consumption and improve the quality of products in olive oil mills (‘almazaras’). In this work, four models of helical separators have been built and tested in order to manage olive oil and water two-phase flows (with olive oil as the majority phase). Separation yields were analyzed from a dimensional analysis perspective, considering variables such as density and viscosity, flow rate, head losses, or the water concentration in the flows studied. The best separation yields (of the order of 80% to 100%) were obtained for olive oil–water two-phase flows in which the water concentrations could be higher, in some cases, than 5–10% for Reynolds numbers of below 60.
En este trabajo se analiza el efecto que la liberalización del mercado eléctrico tiene sobre la v... more En este trabajo se analiza el efecto que la liberalización del mercado eléctrico tiene sobre la variación de los regímenes de temperatura del agua en plantas de acuicultura intensiva que aprovechan los efluentes de refrigeración de centrales generadoras de electricidad. Para ello se han utilizado datos de una instalación dedicada al engorde de anguilas europeas, la cual toma el agua caliente del efluente de refrigeración de la Central Térmica de Puente Nuevo (Córdoba). Los resultados indican que la liberalización del mercado del sector eléctrico tiene una influencia significativa sobre la forma y cantidad de energía generada por la Central Térmica, y por consiguiente sobre el régimen termal del efluente de refrigeración. Los niveles de temperatura en el interior de la instalación son dependientes asimismo de la temperatura del agua en el efluente de refrigeración, estimándose la disminución de los índices de crecimiento debidos a este factor en un 5%.
En este estudio se analiza el crecimiento diario de dos alcornoques situados en una parcela de Hi... more En este estudio se analiza el crecimiento diario de dos alcornoques situados en una parcela de Hinojos (Huelva) en el ano 2004. Los datos de crecimiento se adquirieron cada 15 minutos mediante galgas extensiometricas que se situaron sobre y bajo corcho de cada uno de los individuos. Las variables climaticas y edaficas que intervienen se recogieron cada 15 minutos mediante dispositivos instalados en la parcela. Para cada dia se calculo la maxima contraccion diaria, la maxima dilatacion diaria y el crecimiento diario y se calcularon los valores medios, maximos y minimos de las variables edaficas y climaticas para esos periodos. Con estos se pretendio la caracterizacion de los parametros de crecimiento de los dos alcornoques seleccionados mediante el uso de regresiones multiples y de redes neuronales computacionales, con los datos edaficos y climaticos como variables independientes. Los mejores resultados se consiguieron con los modelos neuronales con correlaciones superiores a 0,6 y m...
Ingeniería del agua, 2007
Desde hace unos años, las redes neuronales computacionales están siendo una de las herramientas m... more Desde hace unos años, las redes neuronales computacionales están siendo una de las herramientas más prometedoras para la estimación de caudales en cuencas. La mayoría de los trabajos de la literatura utilizan para las predicciones, junto con los datos registrados de caudales, otras variables de entrada de carácter hidro-metorológico. En este estudio se analizó el funcionamiento de redes neuronales de retropropagación para la estimación de caudales diarios en cuencas portuguesas, considerando que sólo los datos de caudal de días previos están disponibles para la calibración de los modelos. Además de los modelos tradicionales de redes neuronales que tienen como variables de entrada los caudales en días previos, se realizó un procedimiento de convolución en las neuronas de la capa de entrada y se probó una metodología híbrida combinando redes neuronales computacionales y modelos ARIMA. Los modelos neuronales complementados con un proceso de convolución dieron las mejores estimaciones c...
XLII JORNADAS DE AUTOMÁTICA : LIBRO DE ACTAS
Este artículo describe una propuesta para modificar la arquitectura de control de un Vehículo Ope... more Este artículo describe una propuesta para modificar la arquitectura de control de un Vehículo Operado Remotamente (ROV) destinado a la toma de datos e inspección de granjas acuícolas. La propuesta se basa en incluir un nuevo módulo de Localización, encargado de posicionar distintos tipos de elementos que se encuentren alrededor del ROV y puedan ser de interés para la explotación de la instalación. El proceso de localización se basa en el uso de un sensor de ultrasonido en conjunción con un sistema de localización del ROV, cuya combinación posibilita el posicionamiento local y global de los elementos de interés. El artículo describe el método de localización desarrollado y presenta resultados experimentales que validan la propuesta.
Estuarine, Coastal and Shelf Science
Journal of Irrigation and Drainage Engineering, 2003
ABSTRACT Time should be considered in carrying out the design and management of demand irrigation... more ABSTRACT Time should be considered in carrying out the design and management of demand irrigation distribution systems. In this paper, a method to characterize the pumping flow in demand pressurized systems throughout the day and irrigation season is presented. This method considers the temporal evolution of water requirements during the irrigation season and water demand concentration in certain periods of the irrigation day due to different electrical energy charges. The model was established based on data from an actual water distribution network of an irrigation district in southern Spain. The results differed significantly from those obtained using approaches based on establishing a uniform working probability for the outlets of the water distribution network at all hours of the irrigation day, which underestimated the circulating flows or system capacity. The most probable pumping flow with uniform probability was 3.1 m3/s, a smaller value than those obtained in the off-peak and average energy tariff times (4 and 3.4 m3/s, respectively). The total energy head required at the booster pumping in each period of the irrigation season was simulated. 10,000 randomly chosen scenarios were simulated for each irrigation day and each energy tariff time. The heterogeneous vertical stratification between 50 and 103 m of the required piezometric head was obtained as a function of the demanded flow for the water distribution system. This paper includes a pump selection algorithm for recommending least cost or optimum pump combinations in the distribution network and to evaluate the system's energy cost. The pump recommendations show that the optimal solution could have saved 41% of the pumping cost of the Fuente Palmera irrigation district.
Journal of Irrigation and Drainage Engineering, 2003
One of the main problems in the management of large water supply and distribution systems is the ... more One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models ͑exponential smoothing and ARIMA models͒, and computational neural networks ͑CNNs͒ are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are ͑1͒ water demands from previous days; ͑2͒ climatic data from previous days ͑maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration͒; ͑3͒ crop data ͑surfaces and crop coefficients͒; and ͑4͒ water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.
Fisheries Research, 2009
In this study the performances of computational neural networks (CNNs), multiple linear regressio... more In this study the performances of computational neural networks (CNNs), multiple linear regressions (MLRs) and generalised additive models (GAMs) to predict Pacific sardine (Sardinops sagax) landings and to analyse their relationships with environmental factors in the north area off Chile were studied. For this purpose several local and global environmental variables and indexes (sea surface temperature, sea level and Ekman transport index in the Chilean coast and, sea surface temperature in the area Niño 3 + 4 and Niño 1 + 2, and the south oscillation index) were considered as inputs or independent variables. Additionally, several CNNs were calibrated and validated adding the anchovy (Engraulis ringens) landings in the same area as model inputs. The time lags of the variables considered were selected through analysis of the non-linear cross-correlation functions and an alternative form of sensitivity analysis based on the approach of the missing value problem. The analysis of error measures with validation data set showed that the best results were obtained when local and global variables were used separately and combined with anchovy landings. Globally, the best result was given by a CNN with 18 input variables (model CNN 6(II) which only considered global variables and anchovy landings) and 10 neurons in a hidden layer. For this configuration the explained variance was slightly higher to 86% which supposed a standard error of prediction of 7.66%. These results were significantly better than those obtained with MLRs and GAMs. The strong correlation between predicted and observed sardine landings suggests that CNNs captured the trend of the historical data. Also, the generalisation capacity together the sensitivity analysis allowed us to identify the variables with a high weight in the model and partially to interpret the statistical functional relationships between these environmental variables and sardine landings.
Fisheries Research, 2010
Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. None... more Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. Nonetheless, such techniques are limited by problems of species identification. Several methods and techniques have been used in addressing the problem of acoustic identification species. In this paper, schools of anchovy, common sardine, and jack mackerel were classified using support vector machines (SVMs) and two types of supervised artificial neural networks (multilayer perceptron, MLP; and probabilistic neural networks, PNNs) during acoustic surveys in south-central Chile. Classification was done using a set of descriptors for the schools extracted from the acoustic records. The problem was approached through two multi-class SVMs classifiers: one-species-against-one (1-vs-1) and one-species-against-the-Rest (1vs-R). Multi-class classifications showed that the MLP neural network and SVM approach performed better than the PNN. The classification rates averaged 79.4% with PNN and 89.5% with MLP and SVM.
Biosystems Engineering, 2007
Information regarding water demand is key to managing consumption in irrigation districts. Foreca... more Information regarding water demand is key to managing consumption in irrigation districts. Forecasting water demand is one of the main problems for designers and managers of water delivery systems. This paper evaluates the performance of linear multiple regressions and feed ...
... Sánchez, Antonio Gil, Juan Carlos Martos, Rafael Rodríguez, Mª del Mar Anguita, David Lozano,... more ... Sánchez, Antonio Gil, Juan Carlos Martos, Rafael Rodríguez, Mª del Mar Anguita, David Lozano, M arco Antonio Rodríguez, Sergio Luis Aguilar, Silvina Debelis, y ... Para la obtenci n de la funci n de demanda asociada al caudal ya la altura piezom trica en toda la campa a ...
Aquatic Sciences, 2000
In this paper, we propose an alternative method to predict the Gonadosomatic Index (GSI), based o... more In this paper, we propose an alternative method to predict the Gonadosomatic Index (GSI), based on a technique known as computational neural networks (CNNs), with two main objectives:
Computers and Electronics in Agriculture, 2014
Production functions (PFs) are practical tools for not only irrigation scheduling but also in eco... more Production functions (PFs) are practical tools for not only irrigation scheduling but also in economic analysis as a mathematical relationship between relative grain yield and factors like evapotranspiration, irrigation water and salinity. This study was carried out in the Mashhad region of Iran during cropping years 2010 and 2011 to evaluate the performances of two data mining methods, decision tree and neural network, for deriving PFs of spring wheat under simultaneous drought and salinity stress compared with four well known regression-based PFs. The four well known PFs were: Jensen-PF (Jensen, 1968), Minhas-PF , modified Stewart-PF , and Nairizi-PF . Heading and flowering were the most sensitive growth stages followed by the stem elongation and booting. Salinity stress also affected grain yield and therefore was an important parameter for deriving PFs. In general, all the PFs were in agreement concerning the sensitivity of spring wheat to water stress. The neural network-based PF performed the best with a root mean square error equal to 44.27 g m À2 while the decision tree-based PF ranked fourth out of six in terms of accuracy. The most important advantage of the neural network-based PF was the flexible number of input parameters.
Journal of Irrigation and Drainage Engineering, 2003
ABSTRACT Time should be considered in carrying out the design and management of demand irrigation... more ABSTRACT Time should be considered in carrying out the design and management of demand irrigation distribution systems. In this paper, a method to characterize the pumping flow in demand pressurized systems throughout the day and irrigation season is presented. This method considers the temporal evolution of water requirements during the irrigation season and water demand concentration in certain periods of the irrigation day due to different electrical energy charges. The model was established based on data from an actual water distribution network of an irrigation district in southern Spain. The results differed significantly from those obtained using approaches based on establishing a uniform working probability for the outlets of the water distribution network at all hours of the irrigation day, which underestimated the circulating flows or system capacity. The most probable pumping flow with uniform probability was 3.1 m3/s, a smaller value than those obtained in the off-peak and average energy tariff times (4 and 3.4 m3/s, respectively). The total energy head required at the booster pumping in each period of the irrigation season was simulated. 10,000 randomly chosen scenarios were simulated for each irrigation day and each energy tariff time. The heterogeneous vertical stratification between 50 and 103 m of the required piezometric head was obtained as a function of the demanded flow for the water distribution system. This paper includes a pump selection algorithm for recommending least cost or optimum pump combinations in the distribution network and to evaluate the system's energy cost. The pump recommendations show that the optimal solution could have saved 41% of the pumping cost of the Fuente Palmera irrigation district.
The age structure, growth and reproduction of Lepomis gibbosa (L. 1758) (= L. gibbosus) was studi... more The age structure, growth and reproduction of Lepomis gibbosa (L. 1758) (= L. gibbosus) was studied from March 1993 to September 1994 in a tributary stream of the Guadalquivir River. The maximum age observed was 5+ years both in males and females. In the O+ group, seasonal growth began in February and lasted 8 months. Males and females matured during