Landslide Displacement Prediction Model Integrating Multi-layer Perceptron and Optimized Support Vector Regression

  Objectives  Landslide disaster has become the first geological disaster in China. In the process of landslide movement instability, its surface will show certain deformation information, and this deformation information is an important external manifestation of landslide internal change instability, which can be used as an important reference information for landslide prediction and early warning. Therefore, the high-precision prediction of landslide displacement has important reference value for landslide disaster prediction and early warning.

  Methods  Considering the advantages of the intelligent optimization and machine learning algorithms, a landslide displacement combined prediction model is established. First, the multilayer perceptron (MLP) method is used to preliminary prediction of landslide displacement. Then, the artificial fish swarm algorithm (AFSA) improved based on differential evolution (DE) algorithm is further constructed, and combined with the support vector regression (SVR) to established an optimal combination SVR (OPSVR) to modify the prediction results calculated by MLP.

  Results  The two typical landslides monitored by BeiDou show that: (1) For HF08, the single SVR prediction model had a root mean square error (RMSE) of 23.8 mm between the prediction and the true values on the test set, and RMSE for MLP, genetic algorithm-SVR (GA-SVR), particle swarm optimization-SVR (PSO-SVR) and OPSVR prediction models are 19.9 mm, 10.9 mm, 8.3 mm and 8.3 mm, respectively. Compared with the SVR, MLP, GA-SVR, PSO-SVR and OPSVR prediction models, the prediction accuracy of MLP-OPSVR prediction model is improved by 6.2 times, 5.0 times, 2.3 times, 1.5 times and 1.5 times, respectively. The RMSE of MLP-OPSVR prediction model is only 3.3 mm. (2) For HF05, the single SVR prediction model had a RMSE of 26.5 mm between the prediction and the true values on the test set, and RMSE for MLP, GA-SVR, PSO-SVR and OPSVR prediction models are 32.8 mm, 15.4 mm, 15.6 mm and 15.5 mm, respectively. However, the RMSE of MLP-OPSVR prediction model is only 7.0 mm. Compared with the SVR, MLP, GA-SVR, PSO-SVR and OPSVR prediction models, the prediction accuracy of MLP-OPSVR prediction model is improved by 2.8 times, 3.7 times, 1.2 times, 1.2 times and 1.2 times, respectively.

  Conclusions  The DE algorithm can effectively overcome the outstanding problem that most artificial fish individuals are in random motion in the later stage of AFSA operation and cannot search for the global optimal solution, and improves its optimization performance. Further combined with SVR, it can more reasonably determine the super parameters of SVR and improve its prediction accuracy. Compared with a single MLP and SVR prediction model, as well as combined prediction models of conventional intelligent optimization algorithm (genetic algorithm, particle swarm optimization algorithm) and improved AFSA with SVR, the MLP-OPSVR combined prediction model has higher precision prediction results, and has a good value for popularization and application in landslide warning research.