Elman neural network dynamic prediction based on landslide monitoring data

The landslide is a very frequent geological disaster in China, and its monitoring curve of the accumulative displacement has complex nonlinear property. Researchers have established many prediction models, however, the accuracy of these prediction models is not satisfactory. Based on the Elman neural network which can approximate any arbitrary nonlinear function by arbitrary precision, this paper takes the equation for sigmoid as the kernel function, and uses the method of trial when choosing hidden layer, and through the "3δ" method and normalized engineering instance of landslides to accumulate displacement data, and then Elman neural network dynamic prediction model is established. The model made dynamic prediction to the multiple monitoring data and the results show that the goodness of fit between the model prediction results and the measured data is quite high, and the average error is 1.78%, which means that the prediction accuracy is relatively high, which can verify the Elman neural network can play a role in the prediction of landslide disasters.