Analysis of Grey Neural Network and Its Application in
Landslide Deformation Prediction
1 College of Geoscience and Surveying Engineering, China University of Mining and Technology, D11 Xueyuan Road, Beijing 100083, China〖JP〗
2 School of Surveying and Mapping Engineering, Henan University of Urban Construction, Mingyue Road, Pingdingshan 467044, China〖JP〗
3 Key Laboratory of Mine Spatial Information Technologies,NASMG, 2001 Shiji Road, Jiaozuo 454000, China
Abstract This paper proposes a new model of landslide deformation prediction based on the grey artificial neural network, combining the data processing characteristics of the grey model and the artificial neural network, respectively. There are three kinds of forecasting model structure: series grey artificial neural network (SGANN), parallel grey artificial neural network (PGANN), and inlaid grey artificial neural network (IGANN). The landslide deformation time series is decomposed into trend item and random item in SGANN. The trend item of the deformation time series can be extracted by the grey model, using the artificial neural network to construct the nonlinear relationship between the deformation time series and the trend item. PGANN uses the grey model and the artificial neural network to predict separately, while the weight value of this model is subject to the required accuracy of the experiment. IGANN optimizes the topological structure of the neural network by adding a grey layer and grey model group, in order to reduce the randomness of the original monitoring data and to enhance the model robustness ability. The above three new models are employed to forecast the deformation time series data monitored at the Gushuwu landslide. The cases show that the grey artificial neural network model is valid and feasible in prediction of landslide deformation under complicated conditions.
