上海国土资源2012，Vol.33Issue(2)：74-78,5.
径向基神经网络模型在杭州湾北岸岸线变化中的应用
Application of a Radial Basis Functional （RBF） Neural Network to Examine Shoreline Change on the Northern Margin of Hangzhou Bay
摘要
Abstract
Based on observed data of the annual mean sediment discharge at Datong station and measured profile data from the Longquan–Nanzhu coast,we examine shoreline change within the northern Hangzhou Bay（NHB）,and perform a numerical simulation of shoreline change.The results show coastal erosion within Longquan–Nanzhu harbor in recent decades due to a sharp decrease in sediment discharge from the Yangtze River,the effects of typhoons,and anthropogenic influences.The results of grey relational analysis indicate that temporal variations in the locations of isobaths within the NHB lag behind the reduction in sediment supply from the Changjiang Estuary.A strong positive relation is found among the changes in the locations of various isobaths.Based on the coupling relations between the annual sediment discharge at Datong and changes in the locations of isobaths,a radial basis functional neural network mode（RBF） is established,using input vectors of the annual sediment discharge at Datong,and the locations of isobaths（–3m,–5m,and –8m）.The output vector is the shoreline location（0m isobath） in the following year.The RBF network mode has an error of less than 20%,indicating it is useful for predicting the nature of future shoreline changes.关键词
杭州湾北岸/岸线变化/径向基/神经网络模型Key words
the northern bank of Hangzhou bay/shoreline changes/radial basis function/neural network分类
海洋科学引用本文复制引用
谢华亮,戴志军,彭伟,张小玲..径向基神经网络模型在杭州湾北岸岸线变化中的应用[J].上海国土资源,2012,33(2):74-78,5.基金项目
国家自然科学基金 （）
上海市科技攻关项目 （）
教育部归国留学人员启动基金资助 （）
