Analysis of temporal behavior of climate variables using artificial neural networks: an application to mean monthly maximum temperatures on the Spanish Central Plateau
A forecasting model for the mean monthly maximum temperatures (TMaxMean) using an artificial neuronal network (ANN) of the multilayer perceptron type (Multilayer Perceptron, MLP) has been developed. This model forecast the TMaxMean variable one month ahead after the last data point of the climate se...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Atmósfera |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/26096 |
| Acceso en línea: | https://www.revistascca.unam.mx/atm/index.php/atm/article/view/26096 |
| Access Level: | acceso abierto |
| Palabra clave: | Prognostic models maximum temperature neural networks multilayer perceptron backpropagation |
| Sumario: | A forecasting model for the mean monthly maximum temperatures (TMaxMean) using an artificial neuronal network (ANN) of the multilayer perceptron type (Multilayer Perceptron, MLP) has been developed. This model forecast the TMaxMean variable one month ahead after the last data point of the climate series. The study area considered is the central plateau of the Iberian Peninsula (Castilla y León and Castilla la Mancha). The data series of mean monthly maximum temperature (TMaxMean) were obtained of the observations at the stations of the synoptic and climatological network of the Agencia Estatal de Meteorología (AEMET). The data set is divided into two samples of training and testing. The training data set is used for the model development and the test set is used to evaluate the established model. The parameters of the ANN are fitted experimentally. A supervised training of the MLP ANN is performed. We used a backpropagation (BP) training algorithm with a variable learning rate. After that we evaluated the forecasting skills of the model from the coefficient of determination (R2), the mean square root error (MSE) and the dispersion and sequence graphics of the real and simulated series. The results obtained with the model (indicates a good fit between the real and simulated series) are compared with those obtained with ARIMA models. The results are similar, while the model ANN is able to adjust the extreme values of the real series and certain anomalies, which is not the case with ARIMA models. |
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