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.
Original language | Undefined/Unknown |
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Pages (from-to) | 115-125 |
Number of pages | 11 |
Journal | Engineering Applications of Artificial Intelligence |
Volume | 18 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2005 |
Keywords
- Artificial neural networks
- Decision support
- Forecasting
- Modeling
- Simulation
- Soft computing
- Weather
ASJC Scopus subject areas
- Artificial Intelligence
- Electrical and Electronic Engineering
- Control and Systems Engineering