中文摘要：

针对目前分类算法对降水预测过程存在着泛化能力低、精度不足的问题，提出改进Adaboost算法集成反向传播（BP）神经网络组合分类模型。该模型通过构造多个神经网络弱分类器，赋予弱分类器权值，将其线性组合为强分类器。改进后的Adaboost算法以最优化归一化因子为目标，在提升过程中调整样本权值更新策略，以此达到最小化归一化因子的目的，从而确保增加弱分类器个数的同时降低误差上界估计，通过最终集成的强分类器来提高模型的泛化能力和分类精度。选取江苏境内6个站点的逐日气象资料作为实验数据，建立7个降水等级的预报模型，从对降雨量有影响的众多因素中，选取12个与降水相关性较大的属性作为预报因子。通过多次实验统计，结果表明基于改进的Adaboost-BP组合模型具有较好的性能，尤其对58259站点的适应性较好，总体分类精度达到81%，在7个等级中，对0级降雨的预测精度最好，对其他等级的降雨预测有不同程度的精度提升，理论推导及实验结果证明该种改进可以提高预测精度。

英文摘要：

Aiming at the problem that the current classification algorithm has low generalization ability and insufficient precision, a combination classification model combining Adaboost algorithm and Back-Propagation （BP） neural network was proposed. Multiple neural network weak classifiers were constructed and weighted, which were linearly combined into a strong classifier. The improved Adaboost algorithm aimed to optimize the normalization factor. The sample weight update strategy was adjusted during the lifting process, to minimize the normalization factor, increasing the number of weak classifiers while reducing the error upper bound estimate was ensured, and the generalization ability and classification accuracy of the final integrated strong classifier was improved. A daily precipitation model of 6 sites in Jiangsu province was selected as the experimental data, and 7 precipitation models were established. Among the many factors influencing the rainfall, 12 attributes with large correlation with precipitation were selected as the forecasting factors. The results show that the improved Adaboost-BP combination model has better performance, especially for the site 58259, and the overall classification accuracy is 81%. Among the 7 grades, the prediction accuracy of class-0 rainfall is the best, and the accuracy of other types of rainfall forecast is improved. The theoretical derivation and experimental results show that the improvement can improve the prediction accuracy.