Landslide prediction based on a combination intelligent method using the GM and ENN: two cases of landslides in the Three Gorges Reservoir, China

Abstract

Through the mathematical description of landslide evolution, landslide behavior can be simply studied by the corresponding displacement time sequence. Thus, to predict landslides, a new intelligent prediction method that combines the gray system method (GM) and an evolutionary neural network (ENN) is proposed. In this method, the original displacement is divided into two components: the displacement trend and deviation sequences, which are predicted by the GM and a new ENN combined by immunized continuous ant colony optimization and modified back propagation algorithm. This new method is applied to two typical landslides in the Three Gorges Reservoir of China to show that the forecasting precision of the new method is highly accurate in these cases. For comparison with other methods (GM and ENN), the forecasting performance of the new method is analyzed in depth. The results show that the generalization prediction precision of the new method is much higher than those of the GM and ENN. Finally, the influences of the extrapolation prediction time and the prediction step for the new method are comprehensively analyzed. The results show that the lower the extrapolation prediction time and the prediction step are, the higher the extrapolation prediction precision.