Landslide Susceptibility Evaluation Based on Coupling of GBDT-LR Model and Information Model

Dong Zhangyu; ZhangJin; Peng Peng; Wang Yan; Yang Zhi; An Sen

doi:10.13961/j.cnki.stbctb.2023.01.018

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Landslide Susceptibility Evaluation Based on Coupling of GBDT-LR Model and Information Model

- Landslide Susceptibility Evaluation Based on Coupling of GBDT-LR Model and Information Model
- Bulletin of Soiland Water Conservation Vol. 43, Issue 1, Pages: 149-157(2023)
- 作者机构：
  
  1. 合肥工业大学计算机与信息学院,安徽,合肥,230601
  2. 工业安全与应急技术安徽省重点实验室,安徽,合肥,230601
  3. 智能互联系统安徽省实验室,安徽,合肥,230601
  4. 安徽省地质调查院(安徽省地质科学研究所),安徽,合肥,230001
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2023.01.018
  CLC： P642.22
- Published：2023
- 稿件说明：
移动端阅览
Dong Zhangyu, ZhangJin, Peng Peng, et al. Landslide Susceptibility Evaluation Based on Coupling of GBDT-LR Model and Information Model[J]. Bulletin of Soiland Water Conservation, 2023, 43(1): 149-157.
DOI：

Dong Zhangyu, ZhangJin, Peng Peng, et al. Landslide Susceptibility Evaluation Based on Coupling of GBDT-LR Model and Information Model[J]. Bulletin of Soiland Water Conservation, 2023, 43(1): 149-157. DOI： 10.13961/j.cnki.stbctb.2023.01.018.

摘要

[目的] 探索准确、快速的滑坡易发性区划方法，为区域安全监测提供参考，为政府治理滑坡灾害提供科学依据。[方法] 以安徽省池州市贵池区为研究区域，采用梯度提升决策树—逻辑回归(GBDT-LR)和信息量(I)模型耦合的方法，实现区域滑坡易发性评价。该方法通过对原样本地学习，组合产生新的模拟样本，从而增强易发性评价模型对滑坡的拟合能力；采用Borderline-Smote算法解决样本数据不对称的问题。选用r.slopeunits软件划分的斜坡单元作为最小评价单元，选取坡度、坡向、地形曲率、剖面曲率、平面曲率、地形湿度指数(TWI)、地形起伏度、归一化植被指数(NDVI)、距断裂距离和距水系距离总计10个评价因子。分别从频率比、滑坡灾害点及隐患点密度、ROC曲线3个方面对构建的滑坡易发性模型进行评价。[结果] 试验结果表明：耦合模型I-GBDT-LR分别比I，LR，I-LR模型的高易发区频率比所占比例提升约10%，13%，7%，高易发区滑坡灾害点及隐患点密度分别提升约9，11，7，ROC精度提升约10%，9%，5%。[结论] 从检验指标综合来看，耦合模型的精度均高于单一模型，所提出耦合模型精度又高于I-LR耦合模型，为滑坡易发性评价提供了一种有效的、新型的评价方法。

Abstract

[Objective] The accurate and rapid landslide susceptibility zoning method were studied in order to provide a reference for regional safety monitoring

and provide a scientific basis for the government to control landslide disasters. [Methods] The study was conducted in the Guichi District of Chizhou City

Anhui Province. The coupled model of gradient boosting decision tree-logistic regression (GBDT-LR) and an information value (I) model was used to determine the evaluation of regional landslide susceptibility. The model learns from the original samples and combines them to generate new simulation samples in order to enhance the fitting ability of the model to evaluate landslide susceptibility. The Borderline-Smote algorithm was used to solve the problem of sample data asymmetry. The slope unit divided by r.slopeunits software was selected as the minimum evaluation unit

and a total of 10 evaluation factors were selected: slope gradient

slope aspect

terrain curvature

profile curvature

plane curvature

topographic wetness index (TWI)

topographic relief

normalized difference vegetation index (NDVI)

distance from fault

and distance from river. The landslide susceptibility model was evaluated from three aspects: frequency ratio

density of landslide disaster points and hidden danger points

and the receiver operating characteristic (ROC) curve. [Results] The experimental results showed that the frequency ratio of the coupled model I-GBDT-LR was 10%

13%

and 7% greater than that of the I

and I-LR models

respectively. The density of landslide disaster points and hidden danger points in the high risk area increased by about 9

and 7

respectively

and the ROC accuracy increased by about 10%

and 5%

respectively. [Conclusion] The accuracy of the coupled model was higher than that of the single model

and the accuracy of the coupled model proposed was higher than that of the I-LR coupled model

which provides an effective and new evaluation method for landslide susceptibility evaluation.

关键词

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references

殷坤龙,朱良峰.滑坡灾害空间区划及GIS应用研究[J].地学前缘,2001,8(2):279-284.

朱庆,张曼迪,丁雨淋,等.环境因子空间特征约束的区域滑坡敏感性模糊逻辑分析方法[J].武汉大学学报(信息科学版),2021,46(10):1431-1440.

郭子正,殷坤龙,黄发明,等.基于滑坡分类和加权频率比模型的滑坡易发性评价[J].岩石力学与工程学报,2019,38(2):287-300.

赵建华,陈汉林,杨树锋.滑坡灾害危险性评价模型比较[J].自然灾害学报,2006,15(1):128-134.

罗路广,裴向军,崔圣华,等.九寨沟地震滑坡易发性评价因子组合选取研究[J].岩石力学与工程学报,2021,40(11):2306-2319.

孙小凡,张鹏,党超.基于GIS的城市滑坡灾害易发性评价:以湖北省宜昌市城区为例[J].水土保持通报,2018,38(6):304-309.

许英姿,卢玉南,李东阳,等.基于GIS和信息量模型的广西花岗岩分布区滑坡易发性评价[J].工程地质学报,2016,24(4):693-703.

杜国梁,杨志华,袁颖,等.基于逻辑回归—信息量的川藏交通廊道滑坡易发性评价[J].水文地质工程地质,2021,48(5):102-111.

申怀飞,董雨,杨梅,等.基于AHP与信息量法的甘肃省滑坡易发性评估[J].水土保持研究,2021,28(6):412-419.

崔志超,王俊豪,崔传峰,等.基于层次分析法和模糊数学相结合的甘肃东乡八丹沟泥石流易发性评价[J].中国地质灾害与防治学报,2020,31(1):44-50.

徐胜华,刘纪平,王想红,等.熵指数融入支持向量机的滑坡灾害易发性评价方法: 以陕西省为例[J].武汉大学学报(信息科学版),2020,45(8):1214-1222.

刘福臻,王灵,肖东升.机器学习模型在滑坡易发性评价中的应用[J].中国地质灾害与防治学报,2021,32(6):98-106.

黄发明,潘李含,姚池,等.基于半监督机器学习的滑坡易发性预测建模[J].浙江大学学报(工学版),2021,55(9):1705-1713.

王世宝,庄建琦,郑佳,等.基于深度学习的CZ铁路康定—理塘段滑坡易发性评价[J].工程地质学报,2022,30(3):908-919.

Polat A. An innovative, fast method for landslide susceptibility mapping using GIS-based LSAT toolbox [J]. Environmental Earth Sciences, 2021,80(6):217.

Hung L Q, Van N T H, Duc D M, et al. Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: A case study in the upper Lo River Catchment (Vietnam) [J]. Landslides, 2016,13(5):1285-1301.

Fang Zhice, Wang Yi, Duan Hexiang, et al. Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction [J]. Stochastic Environmental Research and Risk Assessment, 2022,36(10):3535-3556.

Bera S, Upadhyay V K, Guru B, et al. Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India [J]. Natural Hazards, 2021,108(1):1257-1289.

梁丽萍,刘延国,唐自豪,等.基于加权信息量的地质灾害易发性评价: 以四川省泸定县为例[J].水土保持通报,2019,39(6):176-182.

周超,殷坤龙,曹颖,等.基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价[J].地球科学,2020,45(6):1865-1876.

张玘恺,凌斯祥,李晓宁,等.九寨沟县滑坡灾害易发性快速评估模型对比研究[J].岩石力学与工程学报,2020,39(8):1595-1610.

向喜琼.区域滑坡地质灾害危险性评价与风险管理[D].四川成都:成都理工大学,2005.

Alvioli M, Marchesini I, Reichenbach P, et al. Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling [J]. Geoscientific Model Development, 2016,9(11):3975-3991.

赵毅斌,邓必荣,王玺.基于Logistic-信息量耦合模型的地质灾害易发性分区研究:以赣南地区某重点城镇为例[J].地质灾害与环境保护,2021,32(4):45-51.

张凌凡,陈忠辉,周天白,等.基于梯度提升决策树的露天矿边坡多源信息融合与稳定性预测[J].煤炭学报,2020,45(S1):173-180.

Friedman J H. Greedy function approximation: A gradient boosting machine [J]. The Annals of Statistics, 2001,29(5):1189-1232.

He Xinran, Pan Junfeng, Jin Ou, et al. Practical Lessons from Predicting Clicks on Ads at Facebook[M]. ACM, 2014.

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