Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada

Authors:
Imran Maqsood;Muhammad Riaz Khan;Guo H. Huang;Rifaat Abdalla
Affiliations:
Faculty of Engineering, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;AMEC Training and Development Services, 400-111 Dunsmuir Street, Vancouver, British Columbia, V6B 5W3, Canada;Faculty of Engineering, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;GeoICT Lab, York University, 4700 Keele Street, Toronto, Ontario M3J 3P1, Canada
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

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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.