Research On Chaotic Characteristics And Forecasting Of The River Runoff

Research on the river runoff changing rule is the premise and base of reasonable development water resource. Stream flows are sorts of complicated nonlinear time series. For a long time, people research it by using classical certainty methods, random methods, or union of the methods. The deterministic laws onrunoff changs are disclosure. According to the nonline characteristic of hydrographic features, the article uses chaotic theory and confluenses chaotic theory and other methords to to research stream flow. The yellow river is certification object. The main results of this paper are as follows:(1) The phase space reconfiguration is completed about month and day runoff of Lanzhou, Sanmenxia, and Huayuankou station in the yellow river. The results are obtaind. The month runoff phase space reconfiguration's dayly time'Ï„'are 2 and the day runoff are 12. The month runoff embedding dementions are 12, day runoff are 10. The saturation relation demention is 3.5 about day runoff phase space reconfiguration.(2) The chaotic characteristic of runoff time seris is identified adout month and day stream flow in Lanzhou, Huayuankou, Sanmenxia station by using the saturation relation demention, the most Lyapuno index number and PCA. We obtain these results. The first, the month and day cream flow of main station in the yellow river have chaotic characteristic; the second, the actual measurement month runoff time seris'chotic characteristic is stronger than natural in the same hydraulitic station and during the same time. The third, the chaotic characteristic in lower reaches of yellow river is stronger the upper reachs. The 4th, the chaotic characteristic in the modem is stronger than the past. The 5th, the length of runoff time seris can affect chaotic characteristic. The longer is stronger. The 6th, the month runoff chaotic characteristic is stronger than dayrunoff'chaotic characteristic.(3) The river runoff predicting is done by chaotic methords. The phase space neighbor isomtric prediction model is improved. When we predict runoff with this model, the problem of selecting'Ï„'need'nt considered, because of T=Ï„=s 8t', wich eleminits the effect that 'Ï„' affect to the time of predicting'T'.So the predicting time is prolonged, the accuracy is improved. We can predict runoff by advancing one month or one year.(4) Binding the chaotic theory and the support vector machine way, the chaotic predicting model base of least square support vector machine (C-LSSVM) is contructed. By using it in Lanzhou station, we attained the results as follow:the model is fit to predict month runoff, because of using structure risk minimizition principle, solveing the over-fiting problem, fiting to small number.(5) After the ways of overing the problems are proposed,witch are neural network falling into local very minimum point easily, avoding overtraining, and improving the model's extrapolation ablity, the chaotic network model is builded. The phase space reconstruction reveals the complicated information of day runoff seris and the changing law of classtic ways not revealing. By using it in yellow river, the satisfactory result was got.(6) By using Detrended Fluctuation Analysis way to hydraulitic system, the long-range power-law correlations were identified. Analysising 80 year's month runoff seris of Lanzhou station, we concluded that the seris has negative long-range power-law correlations, the relation index is 0.39, and the time length is 11.3a. By analysising it, we conclude the runoff will decress in the 10a in yellow river.