期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 18, 期 1, 页码 115-125出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2004.08.019
关键词
artificial neural networks; decision support; forecasting; modeling; soft computing; simulation; weather
Accurate weather forecasts are necessary for planning our day-to-day activities. However. dynamic behavior of weather makes the forecasting a formidable challenge. This study presents a soft computing model based on a radial basis function network (RBFN) for 24-h weather forecasting of southern Saskatchewan, Canada. The model is trained and tested using hourly weather data of temperature, wind speed and relative humidity in 2001. The performance of the RBFN is compared with those. of multi-layered perceptron (MLP) network, Elman recurrent neural network (ERNN) and Hopfield model (HFM) to examine their applicability for weather analysis. Reliabilities of the models are then evaluated by a number of statistical measures. The results indicate that the RBFN produces the most accurate forecasts compared to the MLP. ERNN and HFM. (C) 2004 Elsevier Ltd. All rights reserved.
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