Abstract: This study aims at improving the accuracy and efficiency of the back propagation(BP) neural network in wheat-stripe-rust prediction.First,the correlation between the atmospheric circulation and the occurrence of wheat stripe rust in China were examined quantitatively.The significant covariates,i.e.factors for the occurrence of wheat stripe rust,were then identified from scores of atmospheric circulation variables at multiple time scales from last January to March.Through the principal component analysis(PCA),the first several components that together explained over 80% of the identified factors were employed as predictors.The prediction experiment was carried out by three models,i.e.the stepwise regression model,the back propagation(BP) neural network model,and the genetic algorithm(GA) optimized BP neural network(GA-BP) model.The experimental accuracy was above 80% with each of the models;while the GA-BP model,with only a quarter of the training epochs of the standard BP neural network,scores the highest accuracy of 92.6%.It was indicated that the prediction of the occurrence of wheat stripe rust could be much promoted using the BP neural network with its network structure simplified through PCA and with its initial weights and threshold optimized by GA.

Key words: Wheat stripe rust, Wheat stripe rust, Atmospheric circulation, Primary component analysis, Genetic neural network, Long-term meteorological prediction