【摘要】 滑坡易发性分析是防灾减灾的重要手段。传统数据驱动模型的性能在很大程度上受到样本数据质量的影响，负样本的随机选取导致滑坡易发性评价缺乏可解释性。为解决这一问题，构建可靠的负样本数据库，本文选取重庆市巫山县库岸段为研究区，引入了一种基于知识-数据协同驱动的滑坡易发性评价方法，将随机森林模型与Scoops 3D相结合，优化负样本的选取策略。使用Scoops 3D评估研究区域的边坡稳定性，得到全域的安全系数值，从安全系数值较大的区域选取负样本，以提高负样本选取的可解释性。本文从模型性能和预测不确定性两个方面对比分析了传统随机森林模型和知识-数据协同驱动模型的差异。结果表明，与缓冲区提取方法相比，安全区域阈值设定为3的知识-数据协同驱动模型AUC均值提高了36.7%，预测不确定性更小。此外，安全区域阈值的选择对预测不确定性和模型性能均有影响。更多还原

【Abstract】 Landslide susceptibility mapping is a crucial tool for disaster prevention and management. The performance of conventional data-driven model is greatly influenced by the quality of the samples data. The random selection of negative samples results in the lack of interpretability throughout the assessment process. To address this limitation and construct a high-quality negative samples database, this study introduces a physics-informed machine learning approach, combining the random forest model with Scoops 3D, to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area. The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’ s simplified method. Instead of conventional random selection, negative samples are extracted from the areas with a high factor of safety value. Subsequently, the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed, focusing on model performance and prediction uncertainty. In comparison to conventional methods, the physics-informed model, set with a safety area threshold of 3, demonstrates a noteworthy improvement in the mean AUC value by 36.7%, coupled with a reduced prediction uncertainty. It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.更多还原