摘要
水文分区问题是模式分类问题的一种,本文采用已被广泛应用于模式分类问题中的自组织特征映射人工神经网络(SOFM网络)方法对江西和福建两省进行水文分区。在水文分区计算中选用了86个水文站点和8个反映流域下垫面因素和水文气候特征的水文因子,利用SOFM神经网络分区方法自动识别子区的个数,较为客观地将江西和福建两省划分为3个水文大区。每个区的平均流域特征与当地的下垫面情况完全吻合,同时,对各站点年最大洪峰流量进行计算和精度检验,其基本满足水文站网规划对水文分区的精度要求,表明用SOFM神经网络方法进行水文分区是一种行之有效的方法。
The self-organizing feature maps (SOFM) neural network, which is widely used in pattern classification of data set, is applied to hydrological regionalization of Jiangsi and Fujian Provinces, China. The basic data obtained from 86 stations are used for calculation. The basic data include topographic and hydro-meteorological factors reflecting the characteristics of catchment. By using this method the number of cluster can be automatically identified. The result shows that three distinct hydrologic regions exist in the territory of these two provinces. The validity of this regional classification is verified by the calculated maximum flood discharge occurred in different hydrological stations.
出处
《水利学报》
EI
CSCD
北大核心
2005年第2期163-166,173,共5页
Journal of Hydraulic Engineering
关键词
水文分区
SOFM神经网络
水文站网
hydrologic regionalization
self-organizing feature maps (SOFM) neural network
hydrologic network
