Abstract:Aiming at how to dig out useful knowledge from the massive meteorological data and improve the accuracy of meteorological forecast, this paper proposed a weather forecast method based on the genetic neural network algorithm on Hadoop platform. The method combined genetic algorithm with neural network algorithm, which could avoid the problem of local optimization in traditional algorithm. Then, the genetic neural network forecasting model is established, and the daily data of the ground climate from 1951 to 2006 of 13 stations in Tianjin is used as experimental data. Finally, the experiment is performed taking the rainfall level as decision attribute, and the results show that the method proposed in this paper can get better prediction accuracy for all rainfall level than traditional neural network algorithm. It has the highest prediction precision for the rainfall level R0 and reaches 87%, which can not only effectively deal with mass meteorological data, but also has high prediction precision and good scalability, it proposes a new way of thinking and method for weather forecast.