The prediction of moisture through the use of neural networks MLP type

Abstract

Neural networks are powerful techniques for non-linear data, which have been proven in many domains. As a result, the artificial neural networks have been applied in various fields including meteorology and climatology. This work is a contribution to the development of methods of weather prediction in general, and humidity rate in particular. In a first step, methods that are based on the study of artificial neural networks types MLP (Multilayer Perceptron) are applied for the prediction of moisture in the area of Chefchaouen in Morocco. In a second step, the proposed new architecture of neural networks of MLP type was compared to the model of multiple linear regression (MLR). The basis of learning neural model was collected between 2008 and 2011 during 1248 days. The latter consists of a number of climatic parameters, such as atmospheric pressure, air temperature, visibility, cloud cover, precipitation, dew point temperature, wind speed and humidity.Predictive models established by the MLP neural networks method are more powerful compared to those established by multiple linear regression, because of the fact that good correlation was obtained with the parameters from a neural approach with a mean squared error 5%.