具有周期变化和下降趋势的地下水位的预测

Pediction of periodic variation and decline tendency of groundwater level

应用门限自回归 (TRA)模型解决具有周期性变化和下降趋势的地下水位的预测问题 ,可以有效地利用地下水位资料所隐含的时序分段相关性 ,起到限制模型误差 ,保证模型预测性能的稳定性 。

A simple and general scheme is presented for establishing the threshold auto regressive (TAR) model. With the accelerating genetic algorithm by the authors, both threshold values and auto regressive coefficients can be optimized, and the difficult problem of modeling of TAR is resolved, which gives a strong tool for widely using TAR model. The result of the calculation example shows that the problem can be successfully resolved for the prediction of the periodic variation and decline tendency of groundwater level with the scheme, that the method is practical and efficient, and that TAR model can effectively utilize the important information of the section interdependence during the time series such as groundwater level dates by controlling threshold valves, can reduce model errors, and can ensure good stability and accuracy of the model forecasting. As a general method, the scheme has major theoretic value and wide range application for predicting nonlinear time series.