Paper Title
Study of the Ann Model Performance Criteria For the Prediction of Time Series Humidity

Abstract
Among the mathematical tools that can meet our needs for the numerical simulation of moisture, we can cite the methods of artificial neural networks (ANN) Multilayer Perceptron type (MLP) and networks with radial basis function (RBF) that constitute an approximation tool of complex systems which are difficult to model by statistical methods classics, such as multiple linear regression (MLR). The performance of these latter in the nonlinear modeling have been proven in many areas of engineering and environmental sciences. In the literature, the artificial neural networks have found great success in the simulation and prediction of environmental parameters and in the development of the meteorological mathematical models.The objective of this study is the development of a mathematical model based on the Multilayer Perceptron Artificial Neural Networks for the moisture prediction. For this purpose, we used a time series of moisture, Measured in the area of Chefchaouen in Morocco, which depends on the air temperature, dew point temperature, atmospheric pressure, visibility, cloud cover, wind speed and precipitation. Furthermore, to choose the best architecture of the MLP neural network, we used several statistical indicators (statistical Criteria). The obtained results of the MLP artificial neural network are discussed and compared to the Multiple Linear Regression (MLR) traditional method. Consequently, MLP method presents a very powerful ability to predict relative moisture. Keywords- Artificial Neural Networks, Criteria Information, Moisture spleen, Multiple Linear Regression, Prediction, mathematical model.