基于SOM神经网络的年径流划分——以密云水库为例

Dividing Annual Runoff Based on SOM Neural Network:Taking Mi-Yun Reservoir as an Example

传统的年径流划分往往通过年径流量的数量指标划分丰水年、平水年与枯水年。但仅仅通过该方法难以反映年内径流分布的情况。引入SOM神经网络,根据径流序列的月径流量、集中度与集中期,年内不均匀系数等指标,进行年径流序列的划分,得出具有年内特征的年径流划分成果。并以密云水库为例,对密云水库年径流进行了应用研究,将密云水库年径流序列划分了丰-平-枯水3大类,在此基础上又细分为9小类。结果发现:不同的分类结果在径流量,年内分布,不均匀性,集中度,年内变化幅度都具有不同的性质。同一大类在径流量相似的基础上,在径流分布,枯水期径流量呈现出不同的特性。说明考虑多种指标的SOM神经网络进行年径流的划分能够更细致地划分各类型的年径流序列。

Traditional annual runoff classification is often divided into wet years,normal years and dry years by formulating quantitative indicators of annual runoff.However,it is difficult to reflect the situation of annual runoff only by dividing the results of annual runoff.This paper introduces the SOM neural network,and divides the annual runoff sequence according to the monthly runoff of the runoff sequence,the average monthly runoff during the year,the uneven coefficient of the year and other indicators,and obtains the results of the annual runoff division with the characteristics of the year.Taking Miyun Reservoir as an example,the annual runoff of Mi-Yun Reservoir is applied to research,and the annual runoff sequence of Miyun Reservoir is divided into 3 categories:wet and normal and dry,and then the results are subdivided into 9 categories.The results indicate that different classification results have different properties in runoff,intra-year distribution,unevenness,concentration,and intra-year change range.The representative years of the 9 sub-categories are similar in runoff in the same major category,and in the runoff distribution,the runoff in the dry season has its own characteristics and different distributions.It shows that the SOM neural network considering multiple indicators can divide the annual runoff sequences in detail.