Prediction method of loess landslides based on faster R-CNN and WACM

Abstract

Due to the complexity of the environment and geological conditions in which the loess slope is located, there are many challenges in the accuracy and prediction of loess landslide detection. Therefore, this study introduces a fast convolutional neural network model to solve the problems of traditional detection methods in terms of technology, cost, and detection accuracy, and to achieve real-time detection of the morphology of loess landslides. A weight absorption coupling model is constructed to address the uniform moisture content in loess with hidden dangers. Combined with instability probability, the probability of shallow loess landslides is predicted. The results showed that the mAP value of the Faster R-CNN algorithm using the ResNet125 network exceeded 90%, which was 46.23% and 32.01% higher than the algorithm models using ResNet50 and VGG16, respectively. The proposed model performed fractal analysis on four different loess particle samples, with correlation coefficients R2 above 0.9. The difference between the predicted and actual moisture content of upper and surface loess was within 11%. Compared with existing methods, the research and construction of a loess landslide detection and probability prediction model has greatly improved reliability and accuracy, which is of great significance for predicting the probability of different loess landslides.