随机森林在降水量长期预报中的应用

Long-term rainfall forecasting based on random forest

随机森林是21世纪提出的基于分类树的算法,在处理大数据集中具有明显优势,首度将其应用在降水长期预报中。以长江中下游地区1月份降水预报为例,运用随机森林模型构建原则,在74项大气环流因子以及前期月降水中筛选模型预报因子,进行长期降水量预报,并将其与神经网络模型预报效果进行对比,发现随机森林的泛化误差为13%,预报准确率达到75%,而神经网络的预报准确率仅为67%。此外,本研究还对长江中下游地区的汛期降水量进行了长期预报,结果表明,随机森林模型进行降水量长期预报中模拟和预报的效果令人满意,值得进一步研究和应用。

Random forest is an algorithm based on classification tree that was proposed in this century.It has obvious advantages in dealing with large data set.In this paper,random forest was applied to predict the long-term precipitation.The Yangtze River region′s precipitation in January was taken as an example,the random forest was used to select the important factors from 74 atmospheric circulation factors,and the precipitation monthly by The National Climate Center forecast was used as prediction factors to predict the precipitation.In addition,the neural network forecasting results were compared.The generalization error of random forest model is 13%,and the forecast accuracy rate is 75%,while the rate of neural network accuracy is 67%.Besides,this study also forecasted the class of precipitation of the flood season in the middle and lower reaches of the Yangtze River region.The results suggested that random forest is worthy of further research and application since the simulation and forecasting of the long-term precipitation is relatively good.