不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律

doi:10.13722/j.cnki.jrme. 2021.0246

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摘要滑坡边界及其空间形状通常呈现为半圆形、簸箕形等不规则多边形面，但部分滑坡易发性建模中采用理想化的滑坡点或缓冲圆作为滑坡边界，给易发性建模带来大量不确定性。为研究不同滑坡边界对建模不确定性的影响规律，以江西上犹县337个滑坡及10个环境因子为数据源，分别基于滑坡点(Point)、缓冲圆(Circle)和准确解译并绘制的多边形面(Polygon)建立滑坡边界及其与环境因子的频率比关联性；之后选取多层感知器(MLP)和随机森林(RF)来构建Point，Circle，Polygon-based MLP和RF等模型，最后采用ROC精度、易发性指数的分布规律及其差异性等开展建模不确定性分析。结果表明：(1) 以滑坡点或缓冲圆作为滑坡边界将增加建模不确定性，而用准确的滑坡多边形边界显然能更有效保证建模精度和可靠性；(2) 基于MLP和RF的滑坡易发性建模不确定性规律一致，但RF模型预测精度高于MLP且其不确定性低于MLP；(3) 以点和缓冲圆为滑坡边界得到的滑坡易发性结果在整体上也能反映研究区滑坡概率空间分布规律，在缺乏准确滑坡边界时可作为一种替补方案。

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	黄发明1
	曹昱1
	范宣梅2
	李文彬1
	黄劲松3
	周创兵1
	范文彦1

关键词 ：边坡工程, 滑坡易发性, 滑坡边界形状, 不确定性分析, 机器学习, 随机森林

Abstract：The landslide boundaries and their spatial shapes usually appear as irregular polygonal surfaces such as semi-circle and dustpan. However，literature review shows that inaccurate landslide point and buffer circle are commonly used as the landslide boundary for landslide susceptibility prediction(LSP)，leading to some uncertainties in the LSP results. To study the effects of different landslide boundaries on the LSP modelling，337 landslides and 10 types of environmental factors in Shangyou County of Jiangxi Province are taken as basic data. The correlations between environmental factors and landslides are built based on different landslide boundaries forms of point，buffer circle and accurate polygonal surface. Next，the multi-layer perceptron(MLP) and random forest(RF) are selected to build six kinds of LSP models，namely，Point，circle and polygon-based MLP and RF models. Finally，three methods are used to analyse the LSP uncertainties，including the ROC accuracy，difference significance analysis and distribution rules of landslide susceptibility indexs. Results show that：(1) LSP uncertainties will increase under the landslide boundaries of landslide point or buffer circle，while the accuracy and reliability of LSP results will increase under the accurate landslide polygon boundaries. (2) The uncertainty rules of LSP obtained by MLP and RF models are consistent，however，the uncertainty of RF is lower than those of MLP. (3) The LSP results of point and buffer circle based models can also reflect the spatial distribution rules of landslide probability on the whole，and can be used as a substitute scheme in the absence of accurate landslide boundaries.

Key words： slope engineering landslide susceptibility landslide boundary uncertainty analysis machine learning random forest

引用本文:

黄发明1，曹昱1，范宣梅2，李文彬1，黄劲松3，周创兵1，范文彦1. 不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律[J]. 岩石力学与工程学报, 2021, 40(S2): 3227-3240.
HUANG Faming1，CAO Yu1，FAN Xuanmei2，LI Wenbin1，HUANG Jinsong3，ZHOU Chuangbing1，FAN Wenyan1. Effects of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction. , 2021, 40(S2): 3227-3240.

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https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme. 2021.0246 或 https://rockmech.whrsm.ac.cn/CN/Y2021/V40/IS2/3227

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