中文摘要：

Verhulst反函数模型适用于非负、单调、监测数据波动小的预测问题，因而对随机波动性较大的数据序列拟合较差，故预测精度低。BP神经网络预测模型适用于随机的非线性动态数据系统，可弥补Verhulst反函数预测模型的局限性。在Verhulst反函数预测预报模型的基础上，增加残差项，运用BP神经网络对残差序列进行二次建模分析，形成滑坡失稳时间预报的Verhulst反函数残差修正模型预测方法。应用Verhulst反函数残差修正模型和Verhulst反函数模型预测方法，对国内外滑坡监测实例进行滑坡预测预报的结果表明，Verhulst反函数残差修正模型预测值的预报结果更接近实际观测数据，预测值的平均相对误差较Verhulst反函数模型减少1％-7％，预测精度高，适用范围广。

英文摘要：

Verhulst inverse-function model is suitable for dealing with such prediction problems as non- negative, monotony and with small fluctuation of the monitoring data. Therefore it is poor for fitting the data sequence that fluctuates at random and is low in forecast accuracy. BP neural network prediction model is applicable to the random, nonlinear and dynamic data system, which can make up the limitation of Verhulst inverse-function prediction and forecast model. On the basis of Verhulst inverse-function prediction and forecast model, and in combination with BP neural network residual series, a new residual correction model of Verhulst inverse-function is built for landslide prediction and forecast. The prediction method of Verhulst inverse-function residual correction model for predicting the time of landslide instability is studied. Both prediction methods of Verhulst inverse-function residual correction model and Verhulst inverse-function model are used to predict the actual landslide cases home and abroad. The research indicates that the prediction and forecasting results of Verhulst inverse-function residual correction model are closer to the practical observation data. The average relative error of the prediction values obtained by Verhulst inverse-function residual correction model is reduced 1%-7% compared with that obtained by Verhulst inverse-function model. It shows that Verhulst inverse-function residual correction model has a higher preci- sion and more extensive application for landslide prediction and forecast.