HE Qian,WANG Ming,LIU Kai.Assessement on Earthquake-Triggered Landslide Susceptibility Based on Logistic Regression and MCMC Method[J].Research of Soil and Water Conservation,2022,29(03):396-403+410.
基于Logistic回归和MCMC方法评价地震滑坡敏感性
- Title:
- Assessement on Earthquake-Triggered Landslide Susceptibility Based on Logistic Regression and MCMC Method
- 文章编号:
- 1005-3409(2022)03-0396-08
- 关键词:
- 马尔可夫链蒙特卡罗; Logistic回归; 不确定性; 地震滑坡; 滑坡敏感性
- Keywords:
- Markov Chain Monte Carlo; Logistic regression; uncertainty; earthquake-induced landslide; landslide susceptibility
- 分类号:
- P642.22
- 文献标志码:
- A
- 摘要:
- Logistic回归模型(Logistic Regression,LR)在滑坡敏感性评价上应用广泛,但目前对于模型参数不确定性的研究较为缺乏。马尔可夫链蒙特卡罗(Markov Chain Monte Carlo,MCMC)方法能够结合参数的先验信息得到其后验分布,从而对估计参数的不确定性进行分析。为探索MCMC方法在Logistic滑坡敏感性模型构建中的有效性; 量化模型参数估计值的不确定性,以西南地区2013年4·20芦山地震,2017年8·8九寨沟地震和2014年8·3鲁甸地震为例,基于MCMC方法对Logistic回归模型的回归系数进行估计。构建了区域的地震滑坡敏感性模型,对模型参数的估计值进行了不确定性分析,并绘制了区域的滑坡敏感性图。结果表明:在芦山地震案例中,模型参数估计值的不确定性都比较低; 在九寨沟案例中,岩性因子的参数估计值不确定性较高; 在鲁甸地震中,岩性、剖面曲率和平面曲率的参数不确定性较高。总的来说,模型中的大多数参数估计值不确定性都较低。所构建的Logistic回归模型在三次地震滑坡事件中的预测精度都较高,AUC(Area Under ROC Curve)值均在0.9以上,这证明了MCMC方法对Logistic模型参数估计的准确性。在三次地震滑坡事件中,因子相对重要性最大的为高程,其次为距离断层的距离以及修正麦卡利烈度。研究为利用LR模型进行滑坡敏感性评价提供了一种新的思路和方法。
- Abstract:
- Logistic regression(LR)model is widely used in the assessment of landslide susceptibility, but the research on the uncertainty of model parameters is relatively lacking. Markov Chain Monte Carlo(MCMC)method can combine the prior information of the parameters to obtain their posterior distribution, thereby analyzing the uncertainty of the estimated parameters. This study aims to explore the effectiveness of MCMC in the susceptibility modelling based on LR and quantify the uncertainty of the estimated parameters of the models. Taking the 2013 Lushan earthquake, the 2017 Jiuzhaigou earthquake and the 2014 Ludian earthquake in southwest China as examples, we estimated the regression coefficients of the Logistic regression model based on the MCMC method, constructed the regional earthquake-induced landslide susceptibility models, analyzed the uncertainty in parameter estimates, and drew the regional landslide susceptibility maps. The results show that: in the Lushan earthquake case, the uncertainty of all the model parameters is relatively low; in Jiuzhaigou case, the uncertainty of the lithology factor is relatively high; in Ludian case, uncertainty in parameter estimates of lithology, profile curvature and plan curvature is relatively high; generally, most parameter estimates have relatively low uncertainty; the Logistic regression models constructed in this study have high prediction accuracy in the three earthquake-induced landslide events, and the AUC(Area Under ROC Curve)values are all above 0.9, which demonstrates the accuracy of using the MCMC method to estimate the coefficients of the Logistic model; for the relative importance of the influencing factors, elevation, distance from the faults and MMI(Modified Mercalli Intensity)are the top three factors for these three cases. This study can provide a new idea and method for using Logistic regression model to assess landslide susceptibility.
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备注/Memo
收稿日期:2020-03-04 修回日期:2020-05-12
资助项目:国家重点研发计划“重大自然灾害多层级精准救助关键技术研究”(211800006)
第一作者:贺倩(1996—),女,山西兴县人,硕士研究生,主要从事自然灾害风险研究。E-mail:heqiancq@mail.bnu.edu.cn
通信作者:汪明(1978—),男,湖北新洲人,博士,教授,主要从事灾害风险评估与管理研究。E-mail:wangming@bnu.edu.cn