# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 1 Introduction
[23] identified the beginning of the landslide acceleration process by using a multi-criteria approach to analyze monitoring displacement data, so as to reduce the false alarms caused by minor landslide acceleration. [PERSON] et al. [24] proposed a phase division method to explore the tangent angle characteristics of the displacement time curve and proposed an early warning method based on the improved tangent angle. However, the improved tangent angle requires the average velocity during the constant deformation phase of the landslide, which is difficult to obtain at the beginning of monitoring. [PERSON] et al. [25] combined the conventional warning method based on the threshold of cumulative displacement, velocity and acceleration, with the method on the basis of normalized tangent angle. They used the time window method to analyze the monitoring data and calculate the warning parameters. They proposed a warning method that divides the warning into normal level and dangerous level. This method improves the warning accuracy, but the parameter determination still depends on experience. [PERSON] et al. [26] proposed a landslide warning method on the basis of displacement back-analysis based on long short-term memory networks (DBA-LSTM) and numerical simulation algorithm. The method was verified in the Shangtan landslide, Guangxi Province, China. However, the prediction ability of DBA-LSTM calculation depends on the correct choice of the model parameter setting. The above methods effectively improve the accuracy of landslide warning, but they all need some a priori parameters, which determine the accuracy of the algorithms. The selection of these parameters relies on experience, which is not robust. The clustering and classification algorithms, such as K-means method and ResNet method, do not set thresholds for early warning, but classify the warning according to landslide deformation state. The K-means method is an unsupervised clustering method, which is widely used for landslide deformation state classification, due to its simple principle, convenient calculation and rapid convergence [27]. The ResNet method is a supervised classification method that uses a residual structure to improve the accuracy of deep network training. It has been widely used in landslide early warning [28]. This study proposes a landslide early warning model based on the K-means-ResNet fast classification algorithm, which does not require setting a priori parameters. Firstly, considering the velocity changes in landslide deformation, we use the K-means algorithm to cluster the deformation states of landslides and define the dangerous categories. Then, the features obtained by gray correlation analysis are used as input features. Based on the ResNet algorithm, we establish a classifier that can automatically recognize the landslide deformation state by adaptively searching for suitable features. In order to verify the performance of the model, the Zhongma landslide in Guangxi Province and Shangmao landslide, in Hunan Province, China, are selected as the study cases, because they are relatively representative landslides in their regions, and there are signs of sliding. They will continue to slide for some time in the future, and the landslide instability will cause more serious economic losses and casualties. Analyzing the data of these two landslides is beneficial to the subsequent early warning. The proposed method is also compared with models such as MLP and LeNet and traditional threshold warning.