论文部分内容阅读
运用GP算法 ,利用长江上游亚高山 12a的降水时间序列 (序列长度为 4 32 )和 9a的径流时间序列 (序列长度为32 4 )构建n维嵌入相空间 ,拟合得到了该区降水时间序列和径流时间序列的关联维数分别为 2 4和 2 8.该值具有不随时间尺度变化而变化的特性 ,意义在于可以从某一时段降水或径流时间序列的信息中获知更长时段的信息 ,也可以从长时段序列信息中提取某一时段的信息 .而且该分维数的求取可以说明至少有 3个因子在影响着该区降水和径流的时间序列动态变化
The GP algorithm was used to construct the n-dimensional embedding phase space using the precipitation time series (sequence length of 4 32) and 9 a of runoff time series (sequence length of 32 4) in the upper reaches of the Yangtze River. The precipitation time series And the correlation dimension of runoff time series are 24 and 28, respectively, which have the characteristics of not changing with the time scale. The significance lies in that the information of longer time period can be obtained from the information of precipitation or runoff time series in a certain period, The information of a period of time can also be extracted from the information of the long time series, and the calculation of the fractal dimension shows that there are at least three factors affecting the time series of the precipitation and runoff in this area