###### Abstract  
A regional-scale landslide early warning system was developed in collaboration with a city government. The structure and distinctive features of the system are described in detail. This system employs the principles of the sequential evaluation method that consecutively applies three different evaluation stages: statistical, physically based, and geomorphological evaluations. Based on this method, the system determines five phases of warning levels with improved levels of certainty and credibility. In particular, the warning levels are systematically derived to enable the discrimination of slope failures and debris flows. To provide intuitive and pragmatic information regarding the warning capabilities of the system, a comprehensive performance analysis was conducted. Early warning level maps were generated and a historical landslide database was established for the study period from 2009 to 2016. As a result, 81% of historical slope failures and 86% of historical debris flows were correctly predicted by high-class warning levels. Miscellaneous details associated to the timing efficiency of warnings were also investigated. Most notably, five high-class warning level events and four landslide events were recorded for a study region during the eight-year period. The four landslide events were all successfully captured by four out of the five warning events. Keywords:landslide; slope failure; debris flow; early warning system; performance analysis; historical landslide data; sequential evaluation method +
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feature involving the implementation of empirical rainfall or hydrological thresholds that rely on statistical analyses of historical rainfall and landslide information. Although these are widely preferred and convenient tools, such thresholds have been mostly oriented toward providing temporal decisions regarding early warnings without taking into consideration the effects of geophysical and geo-hydrological properties that are spatially variable; in rare cases, researchers such as [PERSON] et al. [20] suggested various regional thresholds that consider variations in environmental settings such as topography and lithology. As a result, a significant portion of past and present operational regional-scale LEWSs have not been favorable options in spatially constraining early warning information and providing physical bases. Whereas physically based landslide prediction models are actively suggested and examined in the academic field, it is still rare to find purely physically based models that have progressed to the level of achieving successfully operational LEWS at a regional scale [21]. The evaluation of the regional-scale LEWS performance is another important element in constructing a validated and credible LEWS. Nevertheless, no clear standard has been suggested as of yet to systematically evaluate the early warning performance of LEWSs, and in many cases LEWSs were operated without quantitative or qualitative performance analyses [8]. The majority of the cases that analyzed the performance of LEWSs were basically rooted on the concept of contingency tables [22] that combine two types of binary outcomes in general: true positive (event occurrence with prediction), true negative (no event occurrence with no prediction), false positive (no event occurrence with prediction; false prediction), and false negative (event occurrence with no prediction; missed prediction). From this concept, a variety of useful performance indicators [23] and skill scores [24; 25] have been devised to measure the prediction capability of models. However, although such indicators and scores are used as popular and effective indices for evaluating models, they may be insufficient to a certain degree from the perspective of LEWS users (i.e., the public or decision-makers) for the following reasons. First, different terminologies are used interchangeably and it is sometimes difficult to intuitively recognize the meanings of certain terms.