基于在线SVM的平原河网河道水位预报方法

Water Level Forecast Method of Plain River Network Based on Online SVM

针对平原河网地区河道水位预报的不确定性特征,以京杭运河代表站拱宸桥站为例,提出了一种基于在线支持向量机的河道水位预报方法。根据拱宸桥站河道水位影响因子,选取京杭运河2008—2013年11场典型洪水过程的1500组水文数据作为样本,分别构建了固定式、在线增量式和在线剔除式SVM水位预测模型。定性定量比较了不同预见期下3种模型的预测数据,结果表明在线剔除式SVM模型的预报精度要高于其他2种模型。该方法可为研究平原河网地区河道水位过程实时预报提供参考。

Due to the uncertainty characteristics of forecasting river water level of plain river network,this paper took the representative station Gongchenqiao Station of Jinghang Canal as an examples and proposed a forecasting method of river water level based on online support vector machines(SVM).According to the influencing factors of the water level at Gongchenqiao Station,1500 sets of hydrological data from 11 typical flood processes on the Jinghang Canal from 2008 to 2013 were selected as samples and fixed,online incremental and online elimination SVM water level prediction models were constructed.The forecast data of the three models under different forecast periods were qualitatively and quantitatively compared.The results showed that the prediction accuracy of the online elimination SVM model was higher than that of the other two models.This method could provide references for studying the real-time prediction of the water level process in the plain river network.