Amos Mafigiri, Mohd Faisal Abdul Khanan, Ami Hassan Che Din, M. Z. Abdul Rahman
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Factor maps were classified to derive factor classes for each parameter. The dataset was then processed using a weight-of-evidence statistical model to determine the contrast value of each factor class. Contrast value (C) reflects the extent to which each factor class predicts landslide occurrence. The C-weighted factor maps were then combined to derive the landslide susceptibility index (LSI). The LSI enabled visualization of the spatial distribution of susceptibility based on a given combination of factors. Susceptibility maps were prepared for combinations containing only non-anthropogenic parameters and all landslide parameters. The study compared these combinations to determine the influence of anthropogenic factors on total LSI. Similar analyses were conducted to determine the effect of each anthropogenic factor on LSI. The results indicated that lineament density, distance to lineament and distance to road had a significant influence on landslide occurrence. 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引用次数: 0
摘要
本研究旨在评估与人为活动相关的因果因素对安邦查亚市(angang Jaya Municipal Council)位于吉隆坡东北的Bukit Antarabangsa镇滑坡发生的影响。人为因素的选择是基于该镇的快速发展,众多的滑坡记录和山坡开发的强度。该研究使用了一个数据驱动的统计模型,以20个滑坡的清单为基础,确定了最能预测滑坡发生的因素,并评估了与城市发展相关的因素对易感性的影响程度。共17个因子被划分为4个集群(地质、地貌、水文和人为)。对因子映射进行分类,以派生每个参数的因子类。然后使用证据权重统计模型对数据集进行处理,以确定每个因素类别的对比值。对比值(C)反映了每个因素类别预测滑坡发生的程度。然后结合c加权因子图得出滑坡易感性指数(LSI)。大规模集成电路使基于给定因素组合的易感性空间分布可视化。对仅包含非人为参数和所有滑坡参数的组合编制了敏感性图。该研究比较了这些组合,以确定人为因素对总LSI的影响。进行了类似的分析,以确定每个人为因素对LSI的影响。结果表明,坡面密度、与坡面之间的距离和与道路之间的距离对滑坡的发生有显著影响。观察到土地利用/土地覆盖与滑坡发生的强烈相关性,特别是在高易感性地区,其次是与道路距离的影响。研究结果可用于规划建筑密集的滑坡易发地区的基础设施走廊。
Assessing the Influence of Anthropogenic Causal Factors on Landslide Susceptibility in Bukit Antarabangsa, Selangor
This study sought to assess the influence of causal factors related to anthropogenic activities on landslide occurrence in Bukit Antarabangsa, a township northeast of Kuala Lumpur in Ampang Jaya Municipal Council. Anthropogenic factors were chosen based on the township’s rapid growth, numerous landslide records and intensity of hillside development. The study used a data-driven statistical model to identify factors most predictive of landslide occurrence based on an inventory of 20 landslides, and to evaluate the extent to which susceptibility was driven by factors related to urban development. A total of 17 factors were categorized into four clusters (geological, geomorphological, hydro-tographical and anthropogenic). Factor maps were classified to derive factor classes for each parameter. The dataset was then processed using a weight-of-evidence statistical model to determine the contrast value of each factor class. Contrast value (C) reflects the extent to which each factor class predicts landslide occurrence. The C-weighted factor maps were then combined to derive the landslide susceptibility index (LSI). The LSI enabled visualization of the spatial distribution of susceptibility based on a given combination of factors. Susceptibility maps were prepared for combinations containing only non-anthropogenic parameters and all landslide parameters. The study compared these combinations to determine the influence of anthropogenic factors on total LSI. Similar analyses were conducted to determine the effect of each anthropogenic factor on LSI. The results indicated that lineament density, distance to lineament and distance to road had a significant influence on landslide occurrence. A strong correlation with landslide occurrence was observed for land use/land cover, especially in high susceptibility zones, followed closely by the influence of one distance to road factor class. The results could be useful in planning infrastructure corridors in densely built-up landslide-prone areas.