# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 1 Introduction  
Landslide disasters are extremely common around the world and are characterized by their sudden occurrence, wide distribution and being highly destructive [1]. Landslide disasters cause serious damage to the local residents, as well as natural resources, ecology and the environment. However, it is difficult to accurately forecast the location, time and intensity of occurrence. Landslide deformation monitoring and early warning help reduce the damages caused by landslide disaster. Therefore, related studies are abundant [2, 3]. With the development of monitoring technology, such as GPS, InSAR technology and 3D laser scanning, huge amounts of high temporal and spatial resolution data have become available. Furthermore, automatic landslide identification technology has been developed, laying the foundation for the development of landslide early warning technology. A landslide monitoring system mainly includes risk identification, monitoring, early warning and emergency response measures. Risk identification is mainly carried out by geological experts to determine whether the slope is at risk by field exploration [4]. Monitoring is an important part of the whole system and the basis of early warning, which consists of various types of sensors, such as rain gauges [5], inclinometers [6], etc. With the development of monitoring technology, the level of landslide monitoring has been significantly improved [7], and gradually transitioned from low precision and manual monitoring in the past to high precision and automated monitoring, such as global navigation satellite system(GNSS), light detection and ranging (LIDAR) [8], 3D laser scanning [9], etc. Compared with conventional monitoring means, the new technology will also have more landslide monitoring data, which lays the foundation for the development of landslide early warning technology. The emergency response measures are the response methods after the landslide occurrence warning, and the relevant person in charge is notified by SMS or email. The early warning part is the key to the whole system, an important means to reduce the loss caused by landslide, and also the core of this study. At present, landslide warning depends on traditional empirical threshold warning methods, which use the threshold value as the discriminating criterion. The threshold value is generally determined according to the landslide development trend combined with expertise experience. Many scholars use landslide displacement rate, tangent angle and rainfall as landslide warning criteria [10; 11; 12; 13; 14; 15] for real-time landslide warning. [PERSON] et al. [16] set a velocity threshold for early warning based on the actual creep of large rock landslides. [PERSON] et al. [17] determined the minimum rainfall intensity and duration as a threshold for landslide warning by analyzing the relationship between landslides and rainfall. [PERSON] et al. [18] used ground-based interferometric synthetic aperture radar (GBInSAR) to monitor landslide deformation and set displacement rate thresholds for early warning. [PERSON] et al. [19] analyzed the relationship between soil moisture and rainfall (rainfall impact was quantified as the 24, 36 and 48 h maximum cumulative rainfall values) to monitor and warm landslides. Mass landslide monitoring results show that landslides of different materials (soil, rock, etc. ), sizes and trigger factors have very different deformation thresholds, so we cannot set one threshold for all landslides. Improper threshold causes false alarm or missing alarm. In the era of big data, data analysis, such as numerical simulation and machine learning, has been used for landslide early warning [20; 21; 22]. Some efforts have been made to analyze landslide deformation data to extract the deformation characteristics and obtain the relationship among trigger factors. Data analysis was employed to select appropriate thresholds for landslide warning. [PERSON] et al.