人民长江Issue(7)：51-54,4.DOI:10.16232/j.cnki.1001-4179.2015.07.014
泥石流固体堆积物粒度分布特征研究
Study of grain distribution characteristics of solid accumulation in debris flow
摘要
Abstract
To objectively and accurately study the relationship between the grain size distribution characteristic of solid in deb-ris flow and the influential factors of debris flow, 23 debris flow gullies in Wudongde Reservoir region are further investigated and analyzed. 16 debris flow influential factors that have closely related to the debris flow's fluid characteristics are chosen, and the kernel principal component analysis ( KPCA) is used to reduce dimension of these influencing factors, and a linearly independent principal component is formed. The results show that largest prediction error is 2. 6%, which demonstrates that the influential factors have complicated mapping relation with each other, also possess well nonlinear correlation.关键词
核主成分分析/神经网络/泥石流/粒度分布/分形维数Key words
kernel principal component analysis/neural network/debris flow/size distribution/fractal dimension分类
天文与地球科学引用本文复制引用
徐新川,陈剑平,单博..泥石流固体堆积物粒度分布特征研究[J].人民长江,2015,(7):51-54,4.基金项目
国家自然科学基金资助项目(41330636) （41330636）
