数据驱动的中小河流智能洪水预报方法对比研究  

Comparative study of data-driven intelligent flood forecasting methods for small-and medium-sized rivers

作　　者：马凯凯 李士进[1] 王继民[1] 余宇峰[1] 

机构地区：[1]河海大学计算机与信息学院,江苏南京210098

出　　处：《中国科学技术大学学报》2016年第9期774-779,共6页JUSTC

基　　金：公益性行业科研专项(201501022);江苏省重点研发计划(BE2015707)资助

摘　　要：近几年在洪水预报中,数据驱动洪水预报模型得到了广泛的应用,并取得了良好的效果,但是数据驱动模型大都用于大流域,很少用于小流域.中小河流大多位于资料短缺的山丘区,洪水具有突发性强,汇流时间快,预见期短的特点.为此分别构建了SVM模型、BP神经网络模型、RBF网络模型、极限学习机(ELM)模型,并利用所构建的模型对昌化流域进行预报;结果表明,SVM模型和RBF网络模型在低流量区段预测较准确,而且模型预报稳定;BP神经网络模型在高流量区段较准确,但是模型预报结果不稳定;ELM模型预报误差较大,而且预报不稳定;于是采用组合模型方式:低流量区段采用SVM模型或RBF网络模型,高流量区段采用BP神经网络模型,实验结果表明组合模型预报效果更好.In recent years,data driven flood forecasting methods have been widely used in flood forecasting,and good results have been achieved.But most data-driven models are applied to large basins,seldom in small basins.Flash floods in small- and medium-sized rivers,which are mostly located in datapoor mountainous areas in China,are featured by abruptness,rapid concentration and short forecasting time.The support-vector-machine(SVM)model,the BP neural network model,the RBF neural network model and extreme learning machine(ELM)model respectively are established and the used to forecast flash floods in Changhua basin.The results show that the SVM model and RBF network model have accurate prediction in the low flow section with simple parameters while BP network has better performance in the high flow section with less stable forecast results for the low flow section,and that theELM model is not stable with large deviations.As a result,the SVM model or RBF model was adopted for the low flow section,and BP network for the high flow section.This final combination model shows better performance in experiments.

关 键 词：数据驱动模型 中小河流洪水预报 RBF神经网络 极限学习机 

分 类 号：P338[天文地球—水文科学] TP183[水利工程—水文学及水资源]