Verhulst反函数滑坡预测预报模型的改进

Modification of Verhulst inverse function model of landslide forecast

由于位移时间曲线不一定通过第一个数据点,原始Verhulst反函数模型将第一个数据点作为已知条件来求取模型参数的理论依据不充分,可能导致预报误差较大。为了解决该问题,本文基于误差最小原则,将非线性拟合的方法应用到Verhulst反函数模型的参数求取中。两个实例预报结果表明:经非线性拟合确定模型参数后,Verhulst反函数模型在滑坡时间预报上能取得较准确的预报结果,且预报的时间多稍早于滑坡实际发生时间,这说明经非线性拟合确定模型参数后,用Verhulst反函数模型进行滑坡时间预报是可行的,其预报结果可能能起到提前预报的作用。

Duo to the displacement-time curve may not necessarily pass through the first data point, there is no theoretical basis of taking the first displacement data as the known condition to calculate model parameters in the original Verhulst inverse function model, and this may cause a big error. In order to solve the problem, based on the principle of minimum error, the nonlinear fitting method was applied to calculate parameters of Verhulst inverse function model. The prediction results of two forecast examples indicated that the predicted landslide time of Verhulst inverse function model whose model parameters were gotten by using the method of nonlinear fitting are close to the actual landslide occurrence time, and the predicted landslide time are earlier than the actual occurrence time. The research indicates that using Verhulst inverse function model whose model parameters were gotten by using the nonlinear fitting method to forecast landslide time is feasible, and its prediction results may play a role of early warning and forecast.