Study on Displacement Prediction of Landslide Based on Grey System and Evolutionary Neural Network

Abstract

Landslide is a very serious geological disaster. So, how to control the landslide has become a very important work. To control landslide disaster, the forecasting of landslide is a very powerful method. But the development of landslide is a very complicated dynamic system. To describe this system very accurately is very hard. But the measured displacement series can describe the general laws of landslide development. So, some methods for displacement prediction of landslide are proposed. From analysis of those methods, we can find that, the neural network is a good method. Generally, the displacement time series of the landslide can be divided into some sections, such as, even section, periodic section and fluctuant section, et al. For different section, the different method can be taken. In this paper, considering the monotonously increasing character of the displacement time series of the landslide, this time series is divided into two sections, such as, the trend section and the deviation section. Here, based on the principles of displacement decomposition, the trend of displacement time series is extracted by Grey System and the deviation of Grey System is approximated by the new ENN model. In this new ENN model, the architecture and algorithm parameters of neural network are evolved simultaneously through combining modified BP algorithm and Immunized Evolutionary Programming proposed by author. The Immunized Evolutionary Programming is the combination of traditional evolutionary programming with artificial immune system principles. Using the above analysis, a new intelligent prediction method is proposed here. At last, one engineering examples is used to verify calculating effect of the proposed method. Xintan landslide in China is a very famous landslide for its successful prediction. To control this landslide, a lot of measured displacement data are gotten. With those data, our new method is used to forecast the landslide. The results show that the generalization of the new method is good and it can predict the displacement of landslide very well. So, this new method can be used in real engineering practice very well.