Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation

Abstract

Reliable and accurate prediction of landslide displacement is essential for early warning systems, as well as for disaster prevention and mitigation. Machine learning and deep learning algorithms are capable of modeling the relationship between step-like deformation and causal factors for displacement prediction. The ratio of the training set to the testing and the randomness of model parameters affect the assessment of model prediction capacity, which has been neglected in the literature. To take a more integrated view of the displacement prediction model and results, this study develops a hybrid approach that combines random training set length, Monte Carlo simulation, and performance function to evaluate the prediction model. The proposed approach is applied to a typical step-like case of the Jiuxianping landslide in the Three Gorges reservoir area. The uncertainty and variability of two extensively explored neural network models in the prediction of Jiuxianping landslide are systematically explored. The results showed that the probability of failure (and coefficient of variation (COV)) values for the LSTM and RNN models were 16.20% (COV = 0.19%) and 17.12% (COV = 0.42%), respectively. The LSTM model outperformed the RNN model in terms of failure probability, COV, and error distribution. The proposed scheme is worthy of reference and allows for a comprehensive evaluation of the prediction model and results.