## 2 Landslide Early Warning System for Busan: SEM-LEWS
### Development and Structure of SEM-LEWS  
Busan is the second largest city in South Korea with an area that extends up to ~770 km\({}^{2}\) populated by 3.5 million people. The city has historically been considered as a relatively vulnerable location from a geographical perspective due to the frequent landslide disasters that have occurred. A great portion of the city's area is composed of forested mountainous terrain, and an increasing amount of land is being developed for housing and infrastructure to the point where such sites extend out to hillside locations. As a result, physical and social damages incurred by landslides have been persistently reported for several years [26; 27; 28; 29]. The most prevailing landslide types are shallow landslides and debris flows induced by intense rainfall concentrated within several hours [30; 31]. Although the Korea Forest Service operates a nationwide landslide early warning system that uses an empirically defined soil-water-index threshold [32] to share warning information with local governments in Korea, the accuracy and credibility of the warning information has often been limited when applied at the level of public activations. Through an agreement (memorandum of understanding) with the local government of Busan on a mutual working-level cooperation, our research group was able to implement a test-operational citywide landslide early warning system within the disaster safety management division of the Busan local government, in July 2017. Within this context, Figure 2 and Table 1 illustrate the structure and offer a summary of the main specifications of the SEM-LEWS, respectively. The system can be organized into different data sources, a main server, and a GIS-based graphical user interface within the national disaster management platform called Smart Big Board [33]. In the system, two different modules run independently according to temporal hazard evaluation specifications. The \"Nowcasting\" module determines current early warning levels for landslides using near-real-time recorded rainfall data obtained from 51 rain gauges distributed over the Busan area, while the \"Forecasting\" module determines landslide early warning levels 24 h in advance, primarily by using predicted rainfall data obtained from the Korea Meteorological Administration (KMA). The time required for the main server to perform a series of processes ranging from processing raw data for input parameters to generating and visualizing landslide early warning level maps for Busan is between 5 and 8 min, and this processing time is shorter than the time interval (10 min) to call the now-recorded rainfall data. Therefore, in our study, the update frequency of the landslide early warning level map for the \"Nowcasting\" module was set to 10 min, which is considered sufficient for the near real-time  
Figure 1: Conceptual diagram of the sequential evaluation method used for landslide early warnings, including three evaluation stages and hazard interpretations of each warning level [21]. monitoring of sharply increasing landslide hazard levels. Furthermore, the Forecasting module was set to update early warning level maps every 6 h to correspond with the update interval of the rainfall prediction data from the KMA. All calculations related to the decision-making algorithm of the sequential evaluation method were performed by the main server, which also stored all required input parameter datasets and previously generated warning level maps. In the two designated landslide-prone valley sites, ground-monitoring instruments (including volumetric water content and matric suction sensors) were installed to monitor slope hydrological parameters. These data were transmitted every 10 min and stored on the main server. Through a comparative study, the cumulated monitoring data allows for the validation and updating of numerically