考虑周期性变化的地下水埋深预测自记忆模型

地下水埋深的变化过程是一个复杂的非线性过程，现有的各种预测方法普遍难以在实际中应用。该文提出了一种新的预测方法，这种方法针对具有周期性变化规律的地下水埋深时间序列，首先消除不同周期内地下水埋深均值和变幅的影响，并生成新的时间序列；将新的时间序列视为地下水系统的特解，运用系统自记忆性原理，反演导出系统微分方程；由微分方程进一步建立了地下水埋深预测的自记忆模型。实例研究表明，这种方法方便实用且预测结果接近于实际观测值，可推广应用于具有周期性变化规律的其它时间序列。

Self-memory model for predicting groundwater depth series with periodical fluctuation

The change process of groundwater table with time is a complex nonlinear process, so it is difficult using the present various methods to predict the change to be applied commonly in practice. A sort of new prediction method was proposed for the groundwater table series with periodical fluctuation. First, the differences of the mean values and amplitudes of groundwater table series in different periods should be eliminated and a new time series was established. Subsequently it was considered as a particular solution of groundwater system and the differential equation was retrieved based on the principle of system self-memorization. Finally, a self-memorization model of predicted groundwater table was established using the differential equation. The results show that this proposed method is convenient and practical and the predicted values are close to the real values, so it can be popularized and applied to the analysis of other time series with periodical fluctuation.