###### Abstract
For example, \"the probability of detection\", \"true positive rate\", \"hit rate\", and \"sensitivity\" have the same definition but are used interchangeably. Meanwhile, there are several indices with different definitions but intuitively indistinguishable terminologies. For example, it is difficult to intuitively tell apart the different practical meanings between the two different indices, \"the probability of false detection\" and \"the probability of false alarm\". Second, all model indicators based on contingency tables ultimately refer to the degree in which a model can correctly predict events by reducing the frequency of false and missed predictions to a minimum. However, although certain pragmatic information related to early warnings is essentially required, such information is not obtainable from the model indicators. This paper first provides a technical note that briefly describes the development, structure, principles, and techniques of a citywide landslide early warning system that has distinct features in several aspects. The LEWS implemented by the government of Busan, the second largest city in South Korea, is based on the sequential evaluation method proposed by [PERSON] et al. [21] (hereafter referred to as SEM-LEWS). By using time-varying rainfall data, the LEWS applies three consecutive evaluation stages at each time step: statistical, physically based, and geomorphological evaluations. As a result, the LEWS generates an early warning map that can be classified according to five levels at each near-real-time step with improved credibility and spatial constraints. Each of the five warning level classes defines its own unique interpretation based on the theoretical backgrounds of the evaluating thresholds. In addition, the system is designed to enable the discriminative warning of debris flows from other landslide types by assigning the highest warning level. On the other hand, the performance analysis of this study aimed to provide pragmatic information on the comprehensive warning capabilities of the SEM-LEWS in Busan by quantitatively evaluating the conformity of the five warning level classes to their respective theoretical interpretations. The analysis was conducted based on the simulation results of the SEM-LEWS operation that involved generating a series of early warning level maps for the eight-year period from 2009 to 2016. This paper describes in detail the systematic processes involved in establishing an effective historical landslide database that classifies 186 slope failure and 36 debris flow data. With consideration of several distinctive features of the SEM-LEWS, this study addresses the processes that are undertaken to systematically evaluate the early warning performance of the SEM-LEWS from various perspectives. The performance analyses were carried out for two independent categories: prediction statistics of historical landslide data and temporal performance. As a result, the analyses resulted in miscellaneous pragmatic details regarding early warning capabilities in a probabilistic or quantitative form. Insights on the potential limitations of the performance analyses and suggestions for future study are also discussed.