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      泥石流活动性评价方法研究进展

      Research progress of debris flow activity evaluation methods

      • 摘要: 泥石流作为常见的山地灾害之一, 长期以来是山区灾害防治的重难点, 如何准确快速得出泥石流的实际活动性, 是有效应对灾害的关键。通过回顾国内外泥石流活动性评价研究的进展, 采用文献综述法, 从研究方法发展历程、评价内容对比、主流评价模型及不同尺度的评价特点等方面进行系统分析, 梳理了基于降雨与地形的定量分析、基于灾害历史的半定量分析, 以及GIS、RS、数值模拟等技术支持下的定量评价方法, 并比较了单沟与区域尺度评价的异同。研究表明:泥石流活动性评价经历了从定性描述到定量分析的发展历程, 当前研究主要集中于数理统计、多因子作用及计算机技术融合等方法, 但易发性、敏感性、活动性和危险性四者概念的区分仍较为模糊, 活动性评价体系尚未形成统一标准。今后的研究需加强评价指标的优化, 构建更加精细化的动态评价模型, 结合泥石流敏感性、易发性建立完整的活动性评价体系, 并引入大数据、机器学习及数字孪生技术, 以提升评价的精度与时效性, 为泥石流防灾减灾提供科学支撑。

         

        Abstract: As one of the most common mountain hazards, debris flows have long been a focal point and challenge in disaster prevention and mitigation efforts in mountainous regions.Accurately and rapidly assessing debris flow activity is crucial for effective hazard management.This study systematically reviews the research progress on debris flow activity assessment, with a focus on the evolution of evaluation methods, identification of existing research gaps, and exploration of future research directions.By employing a literature review approach, we analyzed the development of research methodologies, compared different evaluation criteria, examined dominant assessment models, and discussed variations in assessment across different spatial scales.The review can categorize assessment methods into three main types: quantitative analysis based on rainfall and topography, semi-quantitative analysis based on historical disaster records, and quantitative methods integrating GIS, remote sensing (RS), and numerical simulations.Additionally, it contrasted single-channel and regional-scale assessments.The findings indicate that debris flow activity assessment has evolved from qualitative descriptions to quantitative analysis, with recent studies predominantly relying on statistical models, multi-factor analyses, and computational techniques.However, distinctions among susceptibility, sensitivity, activity, and hazard remain ambiguous, and a standardized assessment framework has yet to be established.Future research should refine assessment indicators, develop more precise dynamic evaluation models, integrate susceptibility and sensitivity analyses into a comprehensive activity assessment system, and incorporate big data, machine learning, and digital twin technologies to enhance assessment accuracy and timeliness, ultimately providing more robust scientific support for debris flow disaster prevention and mitigation.

         

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