Knowing the source and runout of debris flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo River basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random-walk and Perla et al.'s (PCM) two-parameter friction model components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial cross-validation, including exploring the model's sensitivity 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.

中文翻译：

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优化和验证用于区域泥石流径流建模的重力过程路径模型

了解泥石流的来源和流出有助于制定旨在减轻这些危害的策略。我们在本文中的研究重点是开发一种用于优化区域敏感性建模的跳动模型的新方法，并以智利圣地亚哥安第斯山脉的迈坡河流域上游为例。我们提出了一种两阶段优化方法，用于自动选择用于估计跳动路径和距离的参数。这种方法优化了开源重力过程路径 (GPP) 建模框架的随机游走和 Perla 等人 (PCM) 的双参数摩擦模型组件。为了验证模型性能，我们使用空间交叉验证来评估优化跳动模型的空间可转移性，包括探索模型对样本大小的敏感性。我们还提供了用于可视化参数选择和模型性能不确定性的诊断工具。尽管单个事件的最佳参数存在相当大的差异，但我们发现我们的跳动建模方法在潜在跳动区域的区域预测中表现良好。我们还发现，虽然相对较小的样本量足以实现总体良好的跳动建模性能，但较大的样本量（即≥80 ) 具有更高的模型性能和更低的不确定性，用于估计未知位置的跳动距离。我们预计，这种使用开源 R 软件和自动地球科学分析地理信息系统 (SAGA-GIS) 的自动化方法将使基于过程的泥石流模型更容易访问，从而使研究人员和空间规划人员能够改善区域- 规模危险评估。