使用频率比（FR）模型进行滑坡易发性分析：以孟加拉国班多尔班县为例
Landslide vulnerability analysis using frequency ratio (FR) model: a study on Bandarban district, Bangladesh
AI总结：
本研究采用FR模型结合DEM、卫星图像和降雨数据，分析了孟加拉国班多尔班县13个滑坡影响因素并绘制了滑坡易发性图，模型预测准确率达76.47%，为政策制定和风险减缓提供了重要依据。
摘要：
本研究评估了吉大港山区（CHT）的滑坡易发性，特别关注孟加拉国东南部的班多尔班县。通过采用多学科方法，考察了影响滑坡的13个因素，包括地形特征、土地利用和环境变量。利用FR模型并整合DEM、卫星图像和降雨数据等各种数据集，进行了滑坡易发性测绘。分析表明，陡坡、高海拔、特定坡向和曲率对滑坡易发性有显著贡献。侵蚀、土壤饱和度、排水密度和人类活动等因素也被确定为关键贡献因素。研究强调了土地利用变化的影响，并突出了植被覆盖的稳定作用。生成的滑坡易发性图（LSM）将该区域划分为五个易发区。该模型预测准确率达到76.47%，表明其在预测滑坡发生方面的有效性。此外，研究确定了研究区在过去三十年中的重大变化，强调了人类活动对边坡不稳定性的影响。这些发现为决策者和土地利用规划者提供了有价值的见解，强调了采取积极措施以减轻滑坡风险和确保社区安全的重要性。将这些见解纳入政策框架
英文摘要：
This study assesses landslide vulnerability in the Chittagong Hill Tracts (CHT), specifically focusing on Bandarban district in Southeast Bangladesh. By employing a multidisciplinary approach, thirteen factors influencing landslides were examined, including terrain features, land use, and environmental variables. Utilizing the FR model and integrating various datasets such as DEM, satellite images, and rainfall data, landslide susceptibility mapping was conducted. The analysis revealed that steep slopes, high elevations, specific aspects, and curvature contribute significantly to landslide susceptibility. Factors like erosion, soil saturation, drainage density, and human activities were also identified as key contributors. The study underscored the impact of land use changes and highlighted the stabilizing effect of vegetation cover. The resulting Landslide Susceptibility Map (LSM) categorized the area into five susceptibility zones. The model demonstrated a prediction accuracy of 76.47%, indicating its effectiveness in forecasting landslide occurrences. Additionally, the study identified significant changes in the study area over three decades, emphasizing the influence of human activities on slope instability. These findings offer valuable insights for policymakers and land-use planners, emphasizing the importance of proactive measures to mitigate landslide risks and ensure community safety. Incorporating these insights into policy frameworks
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
