Precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. Precipitation forecasting and alerts management role is responsible for these problems.
Today, Artificial Neural Networks are one of developed method that applied for estimate and predict of parameters that using Intrinsic relation among data.
The purpose of this study is prediction of daily precipitation using daily data of common statistical period , 23 years,(2012-1989) from meteorological stations, by Perceptron Neural Networks and Radial Basis Function in Kerman, Baft, Miandeh Jiroft.
Different combinations of parameters, such as minimum temperature, maximum temperature, average temperature, relative humidity, wind speed and direction and the mean pressure as input for Artificial Neural Network and precipitation was considered as the output of the Network.
The analysis of output results from , Radial Basis Function shown that these models have better accuracy and a high ability to forecast precipitation than Perceptron Neural Networks. The analysis of output results from , Radial Basis Function Neural Networks shown that these models have better accuracy and a high ability to forecast precipitation than Perceptron Neural Networks. such that in the best combination with minimum and maximum parameters, minimum temperature and relative humidity, the wind speed and direction and mean of pressure in Kerman station with correlation coefficient 0.907 and root of mean square error of 0.014 known as the best model for predicting rainfall in this paper.