Algorithms for intelligent prediction of landslide displacements

Abstract

Landslides represent major threats to life and property in many areas of the world, such as the landslides in the Three Gorges Dam area in mainland China. To better prepare for landslides in this area, we explored how several machine learning algorithms (long short term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) might predict ground displacements under three types of landslides, each with distinct step-wise displacement characteristics. Landslide displacements are described with trend and periodic analyses and the predictions with each algorithm, validated with observations from the Three Gorges Dam reservoir over a one-year period. Results demonstrated that deep machine learning algorithms can be valuable tools for predicting landslide displacements, with the LSTM and GRU algorithms providing the most encouraging results. We recommend using these algorithms to predict landslide displacement of step-wise type landslides in the Three Gorges Dam area. Predictive models with similar reliability should gradually become a component when implementing early warning systems to reduce landslide risk.

BS-LSTM: An Ensemble Recurrent Approach to Forecasting Soil Movements in the Real World

“…In the ML literature, the RNN models such as the long shortterm memory (LSTM) have been developed for forecasting soil movements (Xing et al, 2019;Yang et al, 2019;Jiang et al, 2020;Liu et al, 2020;Meng et al, 2020;Niu et al, 2021). These recurrent models possess internal memory and they are a generalization of the feedforward neural networks (Medsker and Jain, 1999).…”

“…Such recurrent models perform the same function for each data input, and the output of the current input is dependent on prior computations (Mikolov et al, 2011). Some researchers have forecasted soil movements by developing a single-layer LSTM model, where the model used historical soil movements in a time series to forecast future movements (Xu and Niu, 2018;Yang et al, 2019;Jiang et al, 2020;Liu et al, 2020;Meng et al, 2020;Xing et al, 2020;Niu et al, 2021). For example, Niu et al (2021) developed an ensemble of the empirical mode decomposition (EEMD) and RNN model (EEMD-RNN) to forecasting soil movements.…”

“…Several research studies have proposed RNN models to forecast soil movements and determine various triggering parameters for such movements (Xu and Niu, 2018;Xing et al, 2019;Yang et al, 2019;Jiang et al, 2020;Liu et al, 2020;Meng et al, 2020;Niu et al, 2021). For example, Yang et al (2019) created an LSTM model for forecasting soil movements in China's Three Gorges Reservoir area.…”

“…The findings indicated that the LSTM model could be used to forecast soil movements. Next, Liu et al (2020) created LSTM, gated recurrent unit (GRU), and random forest (RF) models to forecast soil movements. These models were trained on the data recorded from the Three Gorges Reservoir, China.…”

Practice of artificial intelligence in geotechnical engineering

“…The estimated thicknesses of soil layers were close to those in the real situation, which demonstrates the capacity of the estimation method. Liu et al (2020) adopted the LSTM neural network, the RF algorithm, and the GRU algorithm to predict landslide displacement in the Three Gorges Dam reservoir. Three different landslides, each with step-wise displacement characteristics, were modelled with each of the ML algorithms.…”