年径流预测的遗传模拟退火门限自回归模型

Annual Runoff Prediction Based on GA and Simulated Annealing

根据年径流时间序列资料所隐含的时序分段相依性,用门限自回归模型(TAR)来预测年径流,并研制了TAR建模的一整套简便通用的方案.文中所提出的模拟退火遗传算法,可同时优化门限值和自回归系数,从而解决了TAR建模过程所涉及的大量复杂寻优工作这一难题,为TAR模型的广泛应用提供了强有力的工具.实例计算的结果说明这套方案是可行和有效的.

To effectively utilize information of the section interdependence in the time series of annual runoff, a threshold auto-regressive （TAR） model is proposed to predict annual runoff. A simple and general scheme is presented to establish a TAR model. With an improved genetic algorithm, both the threshold values and auto-regressive coefficients can be optimized, and the problem of TAR modeling resolved, giving a powerful tool for wide application of the TAR model. A case study shows that the scheme is practical and efficient, and the TAR model can successfully reduce model errors and, by controlling the threshold values, ensure good stability and accuracy of the model forecast. As a general method, the scheme has theoretical value and wide range of applications in nonlinear time series prediction.