Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction.Una red neuronal artificial fue utilizada para la predicción de datos de la velocidad de viento a largo plazo (24 y 48 horas en adelanto) en la Ciudad de La Serena (Chile). Para obtener una efectiva correlación y predición, se implementó una optimización de enjambre de particulas para actualizar los pesos de la red. Se emplearon 43800 datos de velocidad de viento (años 2003-2007), y los valores pasados de velocidad del viento, humedad relativa y temperatura del aire fueron utilizados como parámetros de entrada, considerando que estos parámetros meteorológicos se encuentran fácilmente disponibles en todo el mundo. Se estudiaron varias arquitecturas de redes neuronales y la arquitectura optima se determine añadiendo neuronas de forma sistemática y evaluando la raíz del error cuadrático medio (RMSE) durante el proceso de aprendizaje. Los resultados muestran que las variables meteorológicas utilizadas como parámetros de entrada, tienen un efecto positivo sobre el correcto entrenamiento y capacidades predictivas de la red, y que la red neural híbrida puede pronosticar la velocidad del viento horaria con una precisión aceptable, como un RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 y R2 =0.97 para la predicción de la velocidad del viento de 24 horas en adelanto, y un RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 para la predicción de la velocidad del viento de 48 horas en adelanto

Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction

Estimating the Temperature-Dependent Surface Tension of Ionic Liquids Using a Neural Network-Based Group Contribution Method

A neural network-based group contribution method was developed in order to estimate the temperature-dependent surface tension of pure ionic liquids. A metaheuristic algorithm called gravitational search algorithm was employed in substitution of the traditional backpropagation learning algorithm to optimize the update weights of our neural network model. A total of 2307 experimental data points from 229 data sets of 162 different ionic liquid types, such as imidazolium, ammonium, phosphonium, pyridinium, pyrrolidinium, piperidinium, and sulfonium, were collected from the specialized literature. In this database, a wide temperature range from 263 to 533 K, and a wide surface tension range from 0.015 to 0.062 N·m^–1, were covered. The input parameters contained the following properties: absolute temperature, the molecular weight of the ionic liquid, and 46 structural groups that composed the molecule. The accuracy of the proposed method was checked using the mean absolute percentage error (MAPE) and the correlation coefficient (<i>R</i>) between the calculated and experimental values. The results show that, for the training phase, our method presents a MAPE = 1.17% and <i>R</i>= 0.998, while for the prediction phase, the method shows a MAPE = 1.29% and <i>R</i> = 0.991. In addition, the relative contribution of each input parameter was calculated from the optimal weights of the network. Also, the effects of the temperature, molecular weight, and cation and anion types on the estimation of the surface tension were analyzed. Finally, the proposed method was compared with other methods available in the literature. All results demonstrated the high accuracy of our method to estimate the temperature-dependent surface tension for several ionic liquid types

Estimating the Temperature-Dependent Surface Tension of Ionic Liquids Using a Neural Network-Based Group Contribution Method

A neural network-based group contribution method was developed in order to estimate the temperature-dependent surface tension of pure ionic liquids. A metaheuristic algorithm called gravitational search algorithm was employed in substitution of the traditional backpropagation learning algorithm to optimize the update weights of our neural network model. A total of 2307 experimental data points from 229 data sets of 162 different ionic liquid types, such as imidazolium, ammonium, phosphonium, pyridinium, pyrrolidinium, piperidinium, and sulfonium, were collected from the specialized literature. In this database, a wide temperature range from 263 to 533 K, and a wide surface tension range from 0.015 to 0.062 N·m^–1, were covered. The input parameters contained the following properties: absolute temperature, the molecular weight of the ionic liquid, and 46 structural groups that composed the molecule. The accuracy of the proposed method was checked using the mean absolute percentage error (MAPE) and the correlation coefficient (<i>R</i>) between the calculated and experimental values. The results show that, for the training phase, our method presents a MAPE = 1.17% and <i>R</i>= 0.998, while for the prediction phase, the method shows a MAPE = 1.29% and <i>R</i> = 0.991. In addition, the relative contribution of each input parameter was calculated from the optimal weights of the network. Also, the effects of the temperature, molecular weight, and cation and anion types on the estimation of the surface tension were analyzed. Finally, the proposed method was compared with other methods available in the literature. All results demonstrated the high accuracy of our method to estimate the temperature-dependent surface tension for several ionic liquid types

Modulación de la reacción adrenocortical al estrés agudo en poblaciones norteñas y sureñas de Zonotrichia

How animals respond to perturbations of the environment is relevant to the effects of global climate change and human disturbance. The physiological mechanisms underlying facultative responses to unpredictable perturbations of the environment will allow us to understand why some populations are able to cope more than others. This is important for basic biology as well as for conservation. Northern populations of White-crowned Sparrow (Zonotrichia leucophrys), show varying degrees of modulation of the adrenocortical response to acute stress early in the breeding season. These variations are related to a short breeding season at high latitudes and altitudes (up-regulation of the stress response), and possibly degree of parental care (down-regulation of the stress response). Investigations of many taxa from the northern hemisphere indicate these types of modulation are widespread among vertebrates. However, modulation of the adrenocortical response to stress is much less well-known in the southern hemisphere and Neotropical birds present an ideal model system to test whether patterns of hormonal responses to stress in the northern hemisphere are consistent worldwide. Equatorial, high altitude, populations of the Rufous-collared Sparrow (Z. capensis costaricensis), a southern congener of the White-crowned Sparrow, have long breeding seasons, but show no early breeding up-regulation of the adrenocortical responses to stress. This pattern is more similar to mid-latitude, low altitude, populations of White-crowned Sparrows. Whether austral high latitude and altitude populations of the Rufous-collared Sparrows modulate these processes, under presumably similar constraints of mid- to high latitude seasonality in the north, is currently under investigation.Much of the research cited in this review was supported by grant numbers OPP- 9911333 and IBN-0317141 from the National Science Foundation to J.C. Wingfield. RAV acknowledges support from IEB - grant P05-002- ICM