Application of soft computing models to hourly weather analysis in southern Saskatchewan,Canada |
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| Affiliation: | 1. Faculty of Engineering, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;2. AMEC Training and Development Services, 400-111 Dunsmuir Street, Vancouver, British Columbia, V6B 5W3, Canada;3. GeoICT Lab, York University, 4700 Keele Street, Toronto, Ontario M3J 3P1, Canada;1. Universidad Interamaricana de Puerto Rico, Recinto de Bayamón, Dpto. Ingeniería Mecánica, Recinto de Bayamón, 500 Carretera Dr. John Will Harris, PR 00957, USA;2. Escuela de Ingenieros Industriales, Dpto. de Mecánica Aplicada e Ingeniería de Proyectos, University of Castilla-La Mancha, Campus universitario s/n, 02071, Albacete, Spain;3. Renewable Energy Research Institute, Section of Solar and Energy Efficiency, C/ de la Investigación s/n, 02071, Albacete, Spain |
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| Abstract: | 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. |
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