基于梯度正则化联合重构河流水质模型多项参数

为识别河流水质模型参数,首先采用Crank-Nicolson有限差分格式离散控制方程,其次通过附加终端观测值,设计梯度正则化算法重构河流水质模型的多项参数。结果表明,不管是顺直河流的常系数模型,还是天然的线性相关或者线性无关的变系数模型,梯度正则化方法均能快速有效识别模型参数;初值和测量误差对参数重构影响较小,充分证明梯度正则化算法是一种有效稳定地反演河流水质模型参数的方法。

Reconstruction of river water quality model parameters with gradient regularization approach

In order to identify the parameters of the river water quality model, we adopted the Crank-Nicolson finite difference scheme to discrete the control equations first, and then appended several additional terminal observations, and designed an effective gradient regularization algorithm to reconstruct the model parameters. Numerical results show that for both the constant coefficient model of straight channels and the natural coupled or non-coupled variable coefficient model, the proposed method can quickly and effectively identify the model parameters. In addition, the influences of the initial value and measuring error are insignificant, which further demonstrates that the gradient regularization algorithm is effective to inverse the parameters of the river water quality model.