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《Scientia Geographica Sinica》 2002-02
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Application of Projection Pursuit Threshold Regressive Model for Predicting Annual Runoff

JIN Ju liang 1, WEI Yi ming 2, DING Jing 3 (1.School of Civil Engineering, Hefei University of Technology,Hefei, Anhui 230009; 2. Institute of Policy & Management,Chinese Academy of Sciences,Beijing 100080; 3. College of Hydro elect  
Today water resource is very important in the world. The variation of annual runoff not only influences economy and people′s living standards, but also restricts 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 of drainage basin, the annual runoff time series is a very complex dynamic phenomenon varying from region to region and changing with time, which includes lots of past information of all variations and hides many laws. The evolution treads of annual runoff time series are often time irreversible, nonlinear with weak dependence. Now traditional methods for predicting annual runoff usually use linear technique, but the forecasting precision is not satisfactory, owing to complexity of its intrinsic evolutions, and its close and complicated relationships to climate change and other hydrologic effect factors. In order to predict the high dimension complex dynamic systems of the annual runoff system, a new model-projection pursuit threshold regressive (PPTR) model is presented in this paper. A scheme of PPTR modeling is also given to reduce the computational amount, a new function of projection indexes is constructed, the relation of projection value and predicted object can be described with threshold regressive (TR) model, and it is suggested that both the function of projection indexes and the parameters of TR model can be optimized by using a real coded genetic algorithm developed by the authors. The examples of predicting annual runoff show that PPTR model is both practical and effective. PPTR model is simple and general, which overcomes the shortcomings of large amount of computation and difficulty of computer programming in traditional projection pursuit methods, benefits the more applications of projection pursuit, and gives a new approach to resolving the high dimension, nonlinear complex predictive problems.
【Fund】: 国家自然科学基金和长江水利委员会联合资助项目 (5 0 0 9962 0 ) ;; 国家自然科学基金资助项目 (4 98710 18) ;; 安徽省优秀青年科 技基金;; 安徽省自然科学基金 (0 10 45 10 2 )资助项目
【CateGory Index】: P333.1
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