中山大学学报（自然科学版）2012，Vol.51Issue(2)：107-112,6.
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
摘要
Abstract
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 concluded 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%.关键词
中长期水文预报/模型辨识/CIC/Klman滤波Key words
hydrologic forecast/ model identification/ CIC/ Kalman filtering分类
地球科学引用本文复制引用
路剑飞,于吉涛,陈子燊..中长期水文预报的模型辨识及预测研究[J].中山大学学报（自然科学版）,2012,51(2):107-112,6.基金项目
广东省水利科技创新研究资助项目(2011370004209292) （2011370004209292）
