Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model

Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures. Many factors can influence the occurrence of landslides, which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy. Then the generalization ability of the model will also decline sharply when there are only small samples. To reduce the dimension of calculation and balance the model's generalization and learning ability, this study proposed a landslide prediction method based on improved principal component analysis (PCA) and mixed kernel function least squares support vector regression (LSSVR) model. First, the traditional PCA was introduced with the idea of linear discrimination, and the dimensions of initial influencing factors were reduced from 8 to 3. The improved PCA can not only weight variables but also extract the original feature. Furthermore, combined with global and local kernel function, the mixed kernel function LSSVR model was framed to improve the generalization ability. Whale optimization algorithm (WOA) was used to optimize the parameters. Moreover, Root Mean Square Error (RMSE), the sum of squared errors (SSE), Mean Absolute Error (MAE), Mean Absolute Precentage Error (MAPE), and reliability were employed to verify the performance of the model. Compared with radial basis function (RBF) LSSVR model, Elman neural network model, and fuzzy decision model, the proposed method has a smaller deviation. Finally, the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.

Automated summary

The study proposed a landslide prediction method based on improved PCA and mixed kernel function LSSVR model to reduce the dimension of calculation and balance the model's generalization and learning ability, thus improving prediction accuracy. By optimizing parameters using WOA and verifying model performance with multiple indicators, the proposed method showed smaller deviation compared to other models.