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首页> 外文会议>Flood Forecasting and Water Resources Assessment for IAHS(International Association of Hydrological Sciences)-PUB >Prediction of Chaotic Monthly Runoff Series Using Volterra Adaptive Method
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Prediction of Chaotic Monthly Runoff Series Using Volterra Adaptive Method

机译：Volterra自适应方法预测月径流混沌系列。

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It is important to understand the dynamic features of runoff in hydrology and water resources research fields. An attempt is made in this study to characterize and predict runoff dynamics using nonlinear dynamical methods. The proposed procedures for this are: (a) to detect the possible presence of chaos in monthly runoff series using correlation dimension method and Lyapunov exponents method; (b) to design a second-order Volterra adaptive model to meet the need of chaotic series prediction; ( c ) to predict monthly runoff series using Volterra adaptive method. Monthly runoff series from the Second-Songhuajiang basin in the northeast of China are studied as an example. The low correlation dimension (about 5. 1) and the plus maximal Lyapunov exponent (about 0.111) provide convincing evidence for the presence of low - dimensional chaotic behavior. Predictions of noise-reduced runoff time series show promising results. The Volterra adaptive method (correlation coefficient about 0.98) is proved to be superior to the local approximation method (about 0.95) for prediction, indicating the Volterra adaptive method a more effective approach for runoff prediction.
机译：重要的是要了解水文和水资源研究领域中径流的动态特征。本研究尝试使用非线性动力学方法表征和预测径流动力学。为此，拟议的程序是：（a）使用相关维数法和李雅普诺夫指数法检测月径流序列中可能存在的混乱； （b）设计二阶Volterra自适应模型以满足混沌序列预测的需要； （c）使用沃尔泰拉自适应方法预测月径流量序列。以中国东北第二松花江流域的月径流序列为例。低相关维数（约5。1）和最大Lyapunov指数（约0.111）为存在低维混沌行为提供了令人信服的证据。减少噪声的径流时间序列的预测显示出令人鼓舞的结果。事实证明，Volterra自适应方法（相关系数约为0.98）优于局部逼近方法（约0.95），这表明Volterra自适应方法是一种更有效的径流预测方法。

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