摘要：Knowing the source and runout of debris flows can help in planningstrategies aimed at mitigating these hazards. Our research in this paperfocuses on developing a novel approach for optimizing runout models forregional susceptibility modelling, with a case study in the upper MaipoRiver basin in the Andes of Santiago, Chile. We propose a two-stageoptimization approach for automatically selecting parameters for estimatingrunout path and distance. This approach optimizes the random-walk and Perlaet al.'s (PCM) two-parameter friction model components of the open-sourceGravitational Process Path (GPP) modelling framework. To validate modelperformance, we assess the spatial transferability of the optimized runoutmodel using spatial cross-validation, including exploring the model'ssensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance, larger samples sizes (i.e. ≥80) had higher model performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (SAGA-GIS) will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.
