地下水动态预报的多层递阶组合模型

A Multi-Layer Hierarchical Combinative Model for the Prediction of Regional Groundwater Level

地下水系统是一个复杂的随机系统，降水、灌溉等可视为系统的输入，而地下水位则可视作系统的输出。地下水位是一随机动态数据序列，在年际间具有明显的周期性变化。基于此，提出了一种用于描述和分析具有周期性变化的时间序列的新的组合模型一多层梯阶组合模型，该模型是由多层梯阶模型及自回归滑动平均（ＡＲＭＡ）模型构成。在建模中，将原序列分解为年均值、年度变幅和残差三个子序列。针对前两个子序列的时变特征，可采用多层梯阶模型进行模拟，残差序列则可用ＡＲＭＡ模型描述。本文采用这种新的组合模型对区域地下水位动态进行了预报，结果表明预测效果较好。

In this paper,a new combinative prediction model for thd time-series which has a periodic feature is proposed.In the model,the real measured series is decomposed into an annual average,an annual amplitude and a remains series.The multi-layer hierarchical models are used to model and predict for the average and the amplitude series because of their time-varying feature,the remains series is modelled with ARMA model.The case study shows that the model has good results for the prediction of groundwater level dynamics.