中长期水文预报的模型辨识及预测研究

Model Identification and Prediction Research of Medium and Long-term Hydrologic Forecast

针对中长期水文预报的模型辨识进行研究,探讨了预处理、建模数据量和建模方式对于模型预测精度的影响。利用基于有限采样信息准则(FSIC)的组合信息准则(CIC)对模型进行定阶,结合Kalman滤波方法进行非线性预测研究。研究表明:①在进行模型辨识时,如果预处理导致识别的模型复杂度大幅度降低,应通过模型的预测结果对预处理方法的合理性进行检验;②建模数据量应足以反映时序的内在波动性,但并不是越多越好,过多的建模数据量会导致模型的复杂性大幅度增加,在增加计算耗时的同时,也降低了预测的稳健性;③滑动模型主要是改善了较高径流值和径流峰值的预测情况,相对牺牲了较低径流值的预测精度;④Kalman滤波方法全方位、大幅度的提高了径流在各个区段的预测效果,其峰值预测准确率更是高达63.64%。

Model identification of medium and long-term hydrologic forecast is studied in terms of pre- treatment, data length and ways of modeling which are taken as primary factors for the prediction results. Based on finite sampling information criterion （FSIC）, combined information criterion （CIC） is utilized to choose the proper order of the model. Kalman filtering is also used for nonlinear prediction. It is con-cluded that： 1 ） In model identification, reasonability of the pretreatment should be tested through the prediction results from the model if it significantly reduces the complexity of the model. 2） Data length of modeling should be long enough to reflect inherent oscillations of the time series while excessive amount brings in extra complexity, more time-consuming and less robustness. 3 ） Sliding model is better for larger flux and the streamflow peaks prediction, and sacrifices the precise of predicting relatively low run-off. 4） Kalman filtering used as a prediction method of runoff can remarkably raise the forecast effects in any sections of the range with the accuracy rate of peak-prediction up to 63.64%.