This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for t...This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.展开更多
文摘This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.
