对滑坡预测预报的非线性模型，在估计其参数时，传统的方法是将非线性模型在参数的近似值处展开成泰勒级数，并仅取至一次项，然后再应用线性模型参数估计理论进行参数估计。因线性化时略去了二阶及二阶以上的各高次项，所以必然会产生模型误差。介绍了高斯牛顿法的基本原理，并以洒勒山新滑坡为例，在建立该滑坡灰色GM（1，1）模型和Verhulst模型的基础上，运用高斯-牛顿法对两个非线性模型的参数进行优化。计算结果表明，参数优化后各模型的预测精度比优化前各模型的精度有显著提高。说明采用高斯-牛顿法优化非线性模型参数是提高滑坡预测预报精度的一种有效且切实可行的方法。

In estimating parameters of non-linear models for landslide prediction, the traditional method is to develop into Taylor series at the parameter approximate value and only obtain one item, and then handle by linear model. Because of ignoring the items of two steps and above two steps in linearization, model errorwiu occur inevitably. By introducing Gauss--Newton method and taking Saleshan landslide for an example, Gauss--Newton method is used to optimize parameters of the two non linear models on the basis of establis- hing grey GM（1,1） model and Verhulst model of the landslide. Results indicate that the prediction precision of the two models after optimizing parameters is obviously higher than that of the models before optimizing parameters. So, it is an effective and feasible method for improving landslide prediction precision to use Gauss--Newton method to optimize parameters of non linear models.