摘要
提出了一种集成人工神经网络的概念性水文模型,该模型是一种半分布式概念性水文模型,考虑了降雨的空间变异性,流域特征的不均匀性等因素对径流过程的影响。采用遗传算法进行概念性模型参数优选,同时考虑雨量站的空间分布,利用GIS和DEM数据进行流域单元划分;对于每个子流域,考虑模型输入参数和降雨资料的空间分布特性,进行产流计算;在径流演算过程中,利用人工神经网络的非线性映射方法代替传统概念模型中线性叠加方法计算整个流域的出口流量过程。以半湿润的淮河上游支流的大坡岭流域为例,对模型的可行性进行验证,并与单一的新安江模型的结果进行了比较。验证结果表明,集成人工神经网络技术和新安江模型的耦合模型有较好的模拟和预报结果。
A hybrid form of rainfall-runoff models integrating artificial neural networks（ANNs）with conceptual models is proposed.The integrated model is a semi-distributed form of conceptual rainfall-runoff models,in consideration of the spatial variation of rainfall,the heterogeneity of watershed characteristics and their impacts on runoff.Genetic algorithm is used to optimize the parameters of the conceptual model and GIS software and DEM data are used to divide the whole catchment into sub-catchments based on the spatial distribution of rain-gage stations.As a result,in each sub-catchment,runoff generation is simulated in consideration of the spatially distributed model parameters and rainfall inputs.In runoff routing,instead of a linear superposition of routed runoff from all sub-catchments as traditionally performed in a semi-distributed form of conceptual models,artificial neural networks as an effective tool in nonlinear mapping are employed to estimate runoff.The feasibility of the new approach is demonstrated in Dapoling watershed,the upper tributary of Huaihe River basin,and the results of coupling model are compared with those of the Xinanjiang model.Verified results of the model indicate that the approach integrating artificial neural networks with conceptual models presented in this paper can achieve the promising results with acceptable accuracy for flood event simulation and forecast.
出处
《水土保持通报》
CSCD
北大核心
2010年第6期135-138,144,共5页
Bulletin of Soil and Water Conservation
基金
国家自然科学基金项目"基于数字平台的分布式流域水文模型研究"(50309002)
关键词
新安江模型
人工神经网络
遗传算法
耦合模型
半分布式模型
Xinanjiang model
artificial neural networks
genetic algorithm
coupling model
semi-distributed model
