山区公路沿线斜坡物理韧性随机森林评价模型——以四川省茂县为例

薛蒙蒙; 文海家; 林渝; 孙德亮

doi:10.13961/j.cnki.stbctb.2020.04.023

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山区公路沿线斜坡物理韧性随机森林评价模型——以四川省茂县为例

试验研究

- 山区公路沿线斜坡物理韧性随机森林评价模型——以四川省茂县为例
- Random Forest Evaluation Model for Physical Toughness of Slopes Along Mountain Roads -Taking Maoxian County of Sichuan Province as an example
- 水土保持通报 2020年40卷第4期页码：168-175
- 作者机构：
  
  1. 重庆大学土木工程学院,重庆,400045
  2. 山地城镇建设新技术教育部重点实验室,重庆,400045
  3. 库区环境地质灾害防治国家地方联合工程研究中心,重庆,400045
  4. 重庆师范大学 GIS应用研究重庆市高校重点实验室,重庆,401331
- 作者简介：
- 基金信息：
  
  重庆市技术创新与应用示范一般项目（社会民生类）“山地建筑韧性评价大数据分析建模与应用示范”（cstc2018jscx-msybX0310）
- DOI：10.13961/j.cnki.stbctb.2020.04.023
  中图分类号： X9;U41;X43
- 纸质出版：2020
- 稿件说明：
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薛蒙蒙, 文海家, 林渝, 等. 山区公路沿线斜坡物理韧性随机森林评价模型——以四川省茂县为例[J]. 水土保持通报, 2020,40(4):168-175.

Xue Mengmeng, Wen Haijia, Lin Yu, et al. Random Forest Evaluation Model for Physical Toughness of Slopes Along Mountain Roads -Taking Maoxian County of Sichuan Province as an example[J]. Bulletin of Soiland Water Conservation, 2020, 40(4): 168-175.
薛蒙蒙, 文海家, 林渝, 等. 山区公路沿线斜坡物理韧性随机森林评价模型——以四川省茂县为例[J]. 水土保持通报, 2020,40(4):168-175. DOI： 10.13961/j.cnki.stbctb.2020.04.023.

Xue Mengmeng, Wen Haijia, Lin Yu, et al. Random Forest Evaluation Model for Physical Toughness of Slopes Along Mountain Roads -Taking Maoxian County of Sichuan Province as an example[J]. Bulletin of Soiland Water Conservation, 2020, 40(4): 168-175. DOI： 10.13961/j.cnki.stbctb.2020.04.023.

摘要

[目的] 建立山区公路沿线斜坡物理韧性随机森林评价模型，为山区斜坡抗灾工作提供科学参考。[方法] 以四川省茂县为研究区，选取高程、坡度、坡向、坡位、微地貌、曲率、顺逆向坡、归一化植被指数、岩性、距水系距离、距断层距离、距道路距离、多年平均降雨13个斜坡物理韧性评价因子，结合公路沿线498个历史斜坡破坏点，构建斜坡物理韧性评价的地理空间信息数据库。将样本数据按照7∶3的比例分为训练数据和验证数据。采用随机森林方法对训练数据进行训练建模，将得到的模型分别对训练数据、验证数据和样本整体数据进行预测分析。采用混淆矩阵和ROC曲线对模型预测的准确度进行验证。[结果] 评价因子中高程、距道路距离、距水系距离这3个评价因子的权重较大。该模型精度较高，混淆矩阵的精度为98.9%，训练数据集、验证数据集和整个研究区的ROC曲线下的面积（AUC）值分别为1.000，0.870和0.978。模型仿真到整个研究区中，将研究区的物理韧性划分为极低、低、中、高、极高5个等级。[结论] 基于随机森林方法构建的山区公路沿线斜坡物理韧性评价模型具有较高的稳定性以及可靠性。

Abstract

[Objective] A random forest evaluation model for physical toughness of slopes along mountain roads was established in order to provide a scientific reference for disaster prevention in mountain areas. [Methods] Taking Maoxian County

Sichuan Province as the research area

this study selected 13 physical toughness assessment factors of slopes

including elevation

aspect

slope direction

slope position

micro landform

curvature

type of slope

normalized vegetation index

lithology

distance from water system

distance from fault

distance from road and annual average rainfall

and combined with 498 historical slope failure points along the highway

to construct a geospatial information database for the evaluation of slope physical toughness. The sample data was divided into training data and validation data according to the proportion of 7∶3. The random forest method was used to train and model the training data

then the obtained model was used to predict and analyze the training data

the validation data and the overall sample data respectively. And confusion matrix and ROC curve were used to verify the accuracy of the model prediction. [Results] Among the evaluation factors

the weight of elevation

distance from the road

and distance from the water system was higher. The accuracy of the model was high

the accuracy of confusion matrix was 98.9%

and the AUC (area under the ROC curve) values of the training data

the validation data and the overall sample data was 1.000，0.870 and 0.978

respectively. The model was simulated into the entire study area

and the physical toughness of the study area was divided into 5 levels as: extremely low

low

medium

high and extremely high. [Conclusions] The physical toughness evaluation model of the slope along the mountain highway based on the random forest method has high stability and reliability.

关键词

Keywords

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