Short-term Weather Forecasting using CMAC Structure

N. Sarmadi and M. Teshnehlab (Iran)


Forecasting, CMAC structure, Information fusion, and chaotic system.


Weather Forecasting has been one of the most challenging problems around the world for more than half a century. On the other hand, Intelligent modeling has gained a lot of attention and interest recently [1]. In this paper, a CMAC structure for forecasting of three major meteorological parameters (Pressure, Temperature and Relative humidity) is proposed. The availability of historical weather data makes this area highly suitable for Artificial Neural Networks (ANN’s) implementation. ANN’s are able to learn (extract) the relationships between past and current so predict future climatic variable values, combining both time series and regression approaches. Also spatial or temporal information fusion is used to reach better results (from multiple point sources). The power and prediction ability of model is evaluated by graphical (qualitative) and some quantitative criteria (performance functions) like mean absolute error, root mean square of error and correlation coefficient which show that proposed structure is an excellent tool for our goal.

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