Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predictions in the slope-sliding process. In this paper, we used Python to develop the landslide displacement-prediction method based on the eXtreme Gradient Boosting (XGBoost) algorithm, and optimized the hyperparameters through a genetic algorithm to solve the problem of insufficient short-term displacement-prediction accuracy for landslides. We compared the deviation, relative error (RE) and median of RE of predicted values obtained using XGBoost, SVR and RNNs, and the actual value of landslide displacement. The results show that the accuracies of slope displacement prediction using XGBoost and SVR are very high, and that using RNNs is very low during the sliding process. For large displacement values and small numbers of samples, the displacement-prediction effect of XGBoost algorithm is better than that of SVR and RNNs in the sliding process of landslide. There are generally only fewer data samples collected during the landslide sliding process, so RNNs is not suitable for displacement prediction in this scenario. If the number of data samples is large enough, using RNNs to predict the long-term displacement of the slope may also have a much higher accuracy.