基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图

doi:10.13722/j.cnki.jrme.2022.1089

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摘要当前滑坡易发性评价方法存在集成建模策略缺失及假阴性误报现象。首先创新性地利用stacking算法集成随机森林(random forest，RF)和极端梯度提升树(extreme gradient boosting，XGBoost)，用于预测滑坡发生的空间概率；然后采用小基线集干涉测量方法解译2018年1月～2021年9月的104景Sentinel-1A数据，将沿视线的变形速度(Vlos)重新投影为沿斜坡方向(VSlope)；最后采用经验关联矩阵将易发性分级与变形速度分级相结合，以生成滑坡动态易发性图。研究结果表明，stacking-based RF-XGBoost模型具有比决策树(decision tree，DT)，RF，XGBoost，贝叶斯模型(Bayesian network，BN)和多层感知器神经网络(multilayer perceptron neuro network，MLPNN)更好的预测效果和泛化能力。滑坡动态易发性图对变形强烈的区域具有良好的识别效果，降低了3%～8%低易发性占比，提升了2%左右的高～极高易发性占比。现场调查验证了该方法能够在工程活动强烈的区域提高滑坡易发性等级，减少假阴性误报。大周镇极高动态易发区主要分布在长江沿岸及北部凤凰村，应加强监测预警。提出的集成框架及滑坡动态易发性制图技术能够提高区域滑坡的空间辨识度和预警精度，可以作为滑坡灾害区域规划的新手段。

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	曾韬睿1
	邬礼扬2
	金必晶2
	殷坤龙2
	陈熠婷2
	陈虹2

关键词 ：边坡工程, 滑坡动态易发性制图, 集成策略, SBAS-InSAR

Abstract：The existing landslide susceptibility analysis methods have the defects of ensemble modelling strategy and false negative errors phenomenon. Firstly the stacking algorithm integrating random forest(RF) and extreme gradient boosting(XGBoost) to predict the spatial probability of landslide occurrence was innovatively proposed in this paper. Then，the small baseline subset interferometry technology was used to measure 104 Sentinel-1A data from January 2018 to September 2021，and the deformation velocity along the line-of-sight(Vlos) was re-projected to a new velocity along the steepest slope direction(Vslope). Finally，the empirical matrix was considered to combine the susceptibility and deformation rate classification to achieve the landslide dynamic susceptibility map. The results indicate that the stacking based RF-XGBoost model has better prediction and generalization ability than the decision tree(DT)，RF，XGBoost，Bayesian Network(BN) model，and multilayer perceptron neural network(MLPNN). The landslide dynamic susceptibility map has a better identification ability on areas with strong deformation，reducing the proportion of low-susceptibility by 3%–8% and increasing the proportion of high- and very-high-susceptibility by about 2%. The field investigation verified that this method could improve landslide susceptibility and reduce false negative errors in areas with strong engineering activities. Real-time monitoring should be strengthened in the very-high dynamic susceptibility area of Dazhou Town (along the Yangtze River and the northern Fenghuang village). It is concluded that the ensemble framework and landslide dynamic susceptibility mapping strategy proposed in this paper has high spatial identification and early warning accuracy and can be used as a new method for regional planning of landslide disasters.

Key words： slope engineering landslide susceptibility dynamic mapping ensemble strategy SBAS-InSAR

引用本文:

曾韬睿1，邬礼扬2，金必晶2，殷坤龙2，陈熠婷2，陈虹2. 基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图[J]. 岩石力学与工程学报, 2023, 42(9): 2266-2282.
ZENG Taorui1，WU Liyang2，JIN Bijing2，YIN Kunlong2，CHEN Yiting2，CHEN Hong2. Landslide dynamic susceptibility mapping based on stacking ensemble #br# strategy and SBAS-InSAR. , 2023, 42(9): 2266-2282.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2022.1089 或 http://rockmech.whrsm.ac.cn/CN/Y2023/V42/I9/2266

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