门限回归模型在年径流预测中的应用

Application of Threshold Regressive Model to Predicting Annual Runoff

提出了用门限回归模型（ＴＲ）来描述年径流时间上复杂的统计特性，并研制了ＴＲ建模的一套简便通用的方案．用作者改进的遗传算法可同时优化门限值和回归系数，从而解决了ＴＲ建模过程所涉及的大量优化工作这一难题，为ＴＲ模型的广泛应用提供了有力工具．实例的计算结果说明了其可行性和有效性．该方案在其它自然资源中长期预测中也具有广泛的实用价值．

Today water is in great demand. The variation in annual runoff not only influences economy and people's standards of living, but also curbs the economic development. To solve these problems, accurately predicting the variation of annual runoff is indispensable to scientifically utilize water resources. Being the output of a rainfall-runoff system. the annual runoff time series is a complex dynamic phenomenon variating from region to region and changing with time, which includes lots of past information of all variations and hides many laws. Treads of system evolution are often time irreversible. non-linear with weak dependence. Traditional methods for predicting annual runoff usually use linear technology, but the forecasting precision is dissatisfactory, owing to complexity of its intrinsic evolutions, and its close and complicated relationships to climate change and other effect factors. In order to effectively utilize the important information of the section dependence during the time series of annual runoff and its effect factors, and to increase annual runoff forecasting precision, Threshold Regressive (called TR for short) model based on genetic algorithm is suggested to describe and predict annual runoff in this paper. Genetic algorithm is a kind of general optimization methods based on the mechanics of natural selection and natural genetics. which is a general approach to optimization of parameters of non-linear models. A simple and general scheme is presented for establishing TR model with the improved genetic algorithm, named accelerating genetic algorithm (called AGA for short) developed by the authors. Both threshold values and regressive coefficients can be optimized conveniently by using AGA. and the difficulty of TR model is resolved. which gives a strong tool for widely applying TR model to predict non-linear time series. The scheme includes three steps as follows: 1) To determine the regressive items of TR model and the delay time steps by using the technique of correlation analysis. 2) To determine the number of threshold sections and the search ranges of threshold values by using scatter dot figure. 3) To optimize the parameters of TR model based on the criterion of minimizing the fitting errors between TR computed values and observed values of annual runoff by using the improved genetic algorithm. A case study shows that the scheme is simple, practical and efficient. and that TR model can successfully reduce model errors, and can ensure good stability and forecasting accuracy of the model by controlling threshold valves. The scheme can also be applied to mid and long-term prediction in other natural resources.