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Spatial susceptibility analysis of landslide based on PBLC algorithm
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 62, Issue 4, Pages: 54-64(2023)
- 作者机构：
中山大学地理科学与规划学院，广东 广州 510006
- 作者简介：
- 基金信息：
DOI：10.13471/j.cnki.acta.snus.2022D065
CLC： P237Published：25 July 2023，
Published Online：31 March 2023，
Received：02 September 2022，
Accepted：08 November 2022
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黄伟钧,李佳豪,刘子越等.基于PBLC算法的滑坡空间易发性分析[J].中山大学学报(自然科学版),2023,62(04):54-64.
HUANG Weijun,LI Jiahao,LIU Ziyue,et al.Spatial susceptibility analysis of landslide based on PBLC algorithm[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(04):54-64.
黄伟钧,李佳豪,刘子越等.基于PBLC算法的滑坡空间易发性分析[J].中山大学学报(自然科学版),2023,62(04):54-64. DOI： 10.13471/j.cnki.acta.snus.2022D065.
HUANG Weijun,LI Jiahao,LIU Ziyue,et al.Spatial susceptibility analysis of landslide based on PBLC algorithm[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(04):54-64. DOI： 10.13471/j.cnki.acta.snus.2022D065.
摘要
滑坡空间易发性统计模型的构建需要正样本（滑坡点）和负样本（非滑坡点）两类数据，但历史观测数据仅记录了正样本，而负样本的选取容易受到正样本污染，因为没有滑坡记录的地方也可能在过去或未来发生滑坡，从而导致模型的预测精度与稳定性受到影响。针对此问题，将前期提出的半监督学习算法PBLC（positive and background learning with constraints）应用于滑坡空间易发性分析，探讨其解决负样本污染问题的有效性。本文以粤东地区为研究区，选择高程、坡度、坡向、剖面曲率、距离道路最短距离、距离断层线最短距离、距水系最短距离、年平均降雨量、归一化植被指数和地理坐标共11个影响因子作为环境变量。结果表明，与传统的人工神经网络模型相比，基于PBLC算法的预测概率取值范围更为合理，预测结果更加稳定，且预测精度随背景样本数量增加而提高；粤东地区的滑坡灾害高易发区集中于北部和西南区域，坡度和高程是影响该地区滑坡易发性的主要因子。结果表明，半监督学习算法PBLC可以有效解决滑坡统计建模过程负样本污染的问题，提高模型预测精度。
Abstract
Statistical modeling of landslide susceptibility requires both positive （landslide） and negative （non-landslide） samples， but historical records of landslides only contain information on positive data. Selecting negative samples from areas without historical landslide records is problematic because landslides could have occurred without being observed or will occur in the future. This problem is referred to as case-control sampling with contaminated controls， which will affect the predictive accuracy and robustness of statistical models. To address this problem， we propose applying a semi-supervised learning algorithm PBLC （positive and background learning with constraints） and investigate its effectiveness in landslide susceptibility modeling. Taking Eastern Guangdong Province as the study area， we select 11 environmental variables， including elevation， slope， aspect， profile curvature， the shortest distance from roads， the shortest distance from fault lines， the shortest distance from rivers， mean annual precipitation， normalized difference vegetation index， and spatial coordinates， to investigate the effectiveness of the PBLC algorithm. Experimental results show that traditional artificial neural network underestimates the probabilities of landslide occurrences， and the degree of underestimation is affected by the number of negative samples. By contrast， the predicted probabilities of landslide occurrences by PBLC are more accurate and robust. The predicted landslide susceptibility map by PBLC shows that the areas with high susceptibility class are concentrated in the northern and southwestern regions in Eastern Guangdong Province， and slope and elevation are two of the most important factors that affect landslide susceptibility in the study area. We conclude that the semisupervised learning algorithm PBLC is effective in addressing the case-control sampling with contaminated controls in landslide susceptibility modeling.
关键词
滑坡易发性带约束的正样本-背景学习人工神经网络未标记数据粤东地区
Keywords
landslide susceptibilitypositive and background learning with constraintsartificial neural networkunlabeled dataEastern Guangdong Province
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