Abstract: The temperature prediction model indoor based on BP neural network was established,based on meteorological data inside typical solar greenhouse in North China and other meteorological stations.The temperature indoor of 243 meteorological stations was forecasted by the simulation model.Comprehensive meteorological risk assessment model for solar greenhouse was established based on real code accelerating genetic algorithm (RAGA) and projection pursuit evaluate model (PPE),with forecast temperature data indoor and precipitation,sunlight and wind speed from other meteorological stations.The meteorological disaster risk of solar greenhouse in North China was evaluated.The results showed that the standard error was 0.89-1.54℃ between forecasted temperature and observed data,and the determination coefficient was between 0.87-0.94.The highest meteorological disasters risk level of solar greenhouse was from January to March,which was located in North of Tianshan,North of Xing Anling and Tibetan,mainly because of low temperature and frequent winds dust.The lowest risk level of meteorological disasters was in September,which was located between south of the Great Wall and north of the Yellow River,mainly because of low temperature.The meteorological risk evaluation model could provide decision making support for distribution and defence of agro meteorological disaster risk.

Key words: RAGA, BP neural network, Projection pursuit, Solar greenhouse, Risk evaluation