Neurocomputing based Canadian weather analysis

This paper investigates the development of a reliable and efficient neuro-computing technique to forecast the peak weather in Vancouver, British Columbia, Canada. For developing the models, we used one year's data comprising of daily maximum temperature, wind-speed and visibility. This paper briefly explains how neural network models could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust to provide a reliable model for practical peak weather forecasting. Experiment results demonstrate that neuro-forecast models show a very good prediction performance and the approach is effective and reliable.