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首页> 外文期刊>Bulletin of engineering geology and the environment >Assessing debris flow susceptibility in Heshigten Banner, Inner Mongolia, China, using principal component analysis and an improved fuzzy C-means algorithm
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Assessing debris flow susceptibility in Heshigten Banner, Inner Mongolia, China, using principal component analysis and an improved fuzzy C-means algorithm

机译：利用主成分分析和改进的模糊C均值算法评估内蒙古黑石腾旗的泥石流敏感性

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摘要

Susceptibility analysis is important in any study of debris flows. Unlike debris flows in Southwest China, debris flows in Northern China occur with different characteristics and much lower frequency. However, neglecting the danger of possible debris flows in this northern area may result in a devastating disaster. In this paper, 12 debris flow catchments located near Zhirui town, in Heshigten Banner, Inner Mongolia, China, were investigated. A geographic information system, a global positioning system, and remote sensing were used to determine geological, topographical, morphological, and vegetation factors of influence. Principal component analysis was carried out to convert this set of possibly correlated factors into a set of values of linearly uncorrelated principal components. The accumulative contribution rate of the first five principal components retained most of the information on these factors, accounting for 90.9 %. An improved fuzzy C-means clustering analysis was applied to determine the susceptibility of debris flows in this area. This method is based on a quantum-behaved particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for four of the debris flow catchments were high, six were moderate, and two were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.
机译：敏感性分析在任何泥石流研究中都非常重要。与中国西南地区的泥石流不同，中国北方地区的泥石流具有不同的特征，且频率较低。但是，忽视在该北部地区可能发生泥石流的危险可能会造成灾难性的灾难。本文研究了位于内蒙古黑石腾旗直瑞镇附近的12个泥石流集水区。使用地理信息系统，全球定位系统和遥感来确定影响的地质，地形，形态和植被因素。进行了主成分分析，以将这组可能的相关因子转换为一组线性不相关的主成分值。前五个主成分的累积贡献率保留了有关这些因素的大部分信息，占90.9％。应用改进的模糊C-均值聚类分析确定该地区泥石流的敏感性。该方法基于量子行为粒子群优化算法，该算法是一种进化算法，可以实现全局优化，并且对初始聚类中心不敏感。结果表明，四个泥石流集水区的敏感性水平高，六个中等，两个较低。我们基于这些非线性方法的定量评估与现场调查一致。

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