Landslide susceptibility mapping of mountain roads based on machine learning combined model

Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.

Years Spent on Mountain Roads

SONG Fangrong, the Tu nationality girl who grew up drinking water from mountain springs, walked into the Great Hall of the People in Beijing to accept the highest prize for China’s youth—the "May 4th Youth Prize." Not long before, she had been named one of the National Ten Outstanding Youths. She is the only individual to have won both.

Settlement patterns of mountainous half-filled and half-cut widened subgrade with retaining wall

The settlement of widened highway subgrade in mountainous area is not only affected by the interaction between new and existing subgrade, but also seriously restricted by the external retaining wall. Based on the practical engineering of half-filled and half-cut widened mountainous highway subgrade with external balance weight retaining wall(BWRW), a sophisticated finite element numerical model is established. The evolution law of subgrade settlement is revealed during the whole process of new subgrade filling and BWRW inclination after construction. The settlement component of subgrade is clarified considering whether the existing pavement continues to be used. The results show that the additional settlement caused by the BWRW inclination after construction cannot be ignored in the widening and reconstruction of mountainous highway subgrade. In addition, pursuant to the comprehensive design of subgrade and pavement, the component of subgrade settlement should be determined according to whether the existing pavement continues to be used, while considering the influence of BWRW inclination after construction. When the existing pavement continues to be used, the settlement of the existing subgrade is caused by the new subgrade filling and the BWRW inclination after construction. On the contrary, the settlement is only caused by the BWRW inclination after construction.