A Landslide Warning Method Based on K-Means-ResNet Fast Classification Model

Abstract

Landslide early warning is a key technology for effective landslide prevention and control. However, the traditional landslide early warning mainly makes decisions through thresholds, and if the thresholds are not selected properly, it will lead to missing alarms and false alarms frequently. To resolve this problem, this study proposes a landslide early warning algorithm based on a K-means-ResNet model. This method uses the K-means method to cluster the landslide deformation state, and then uses ResNet to classify the landslide rainfall and deformation data, so as to realize the threshold-free judgment and early warning of landslide deformation state. The model was applied to the Zhongma landslide, Guangxi Province, China, and the Shangmao landslide, Hunan Province, China, for validation and evaluation. The results showed that the accuracy, precision and recall of the proposed model can reach 0.975, 0.938, 0.863 and 0.993, 0.993, 0.925, respectively, for classifying the deformation states of the two landslides, and the classification results are better than those of the baseline model. Compared with the threshold-based early warning method, the proposed early warning method does not require artificial determination of threshold parameters and can effectively identify landslide deformation states, which can not only reduce false alarms and missing alarms but also improve the reliability of early warning.