基于随机森林遥相关因子选择的月径流预报北大核心CSCD

流域径流过程与大尺度气候因子之间存在遥相关关系,如何从众多的水文、气象、大气环流及洋流等因子中找出与径流密切关联的因子,是中长期径流预报的一个难题。将基于贝叶斯优化的随机森林模型应用于对水文、气象、气候因子构成的高维度因子集进行因子选择,根据变量重要性评分挑选对月径流影响较大的预报因子,构建广义回归神经网络、极限学习机、支持向量回归径流预报模型。将该方法应用到金沙江流域,相较于线性相关法,基于随机森林输入因子选择的方法提高了模型泛化性能;遥相关因子的引入既实现了流域月径流高精度预报,又从物理机制上提供了支撑。

Monthly runoff prediction based on teleconnection factors selection using random forest model

A teleconnection relationship exists between watershed runoff and large-scale climate indexes. For medium- and long-term runoff prediction, a major difficulty is how to pick out those that are strongly correlated with runoff from various factors such as hydrology, meteorology, atmospheric circulation, and ocean current. This study applies a random forest model based on Bayesian optimization (sequential model-based optimization for general algorithm configuration) to selecting runoff predictors from the set of high-dimensional hydrometeorological and climatic factors according to their importance scores, and constructs a general regression neural network, an extreme learning machine, and a support vector regression runoff prediction models. The method is applied to runoff predictions for the Jinsha River. Compared with those of the factor selection model based on correlation coefficients, our new prediction model using the random forest for factor selection improves the generalization capability. Meanwhile, adding appropriate teleconnection climatic factors to the prediction model inputs can help improve accuracy of monthly runoff prediction and provide physical basis for the model.