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自然资源遥感  2021, Vol. 33 Issue (4): 34-42    DOI: 10.6046/zrzyyg.2020351
  地面沉降监测专栏 本期目录 | 过刊浏览 | 高级检索 |
联合WT-RF的津保高铁沿线地面沉降预测
周超凡1,2,3,4(), 宫辉力1,2,3,4(), 陈蓓蓓1,2,3,4, 雷坤超5, 施轹原1,2,4, 赵宇1,2,4
1.首都师范大学水资源安全北京实验室,北京 100048
2.首都师范大学地面沉降机理与防控教育部重点实验室,北京 100048
3.三维信息获取与应用教育部重点实验室,北京 100048
4.首都师范大学资源环境与旅游学院,北京 100048
5.北京市水文地质工程地质大队,北京 100195
Prediction of land subsidence along Tianjin-Baoding high-speed railway using WT-RF method
ZHOU Chaofan1,2,3,4(), GONG Huili1,2,3,4(), CHEN Beibei1,2,3,4, LEI Kunchao5, SHI Liyuan1,2,4, ZHAO Yu1,2,4
1. Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
2. Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, MOE, Capital Normal University, Beijing 100048, China
3. Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China
4. School of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
5. Beijing Institute of Hydrogeology and Engineering Geology, Beijing 100195, China
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摘要 

地面沉降是一种由多种因素引发的区域地面高程下降的环境地质现象,一定程度上会降低高速铁路的平顺性,影响高速铁路安全运营。针对传统随机森林模型对时序数据预测时未考虑数据内部复杂规律问题,该文构建基于小波变换的随机森林模型(wavelet transform-random forest,WT-RF),预测高铁沿线地面沉降信息,评价地面沉降对高铁坡度变化的影响。研究结果表明,2016—2018年,累积地面沉降影响津保高铁坡度变化范围为0~0.16‰; 基于WT-RF模型对地面沉降预测具有较高精度; 2018—2020年,地面沉降仍呈现加重趋势。津保高速铁路沿线坡度变化范围虽然在0~0.2‰之间,但较目前呈现增大趋势。研究发现地面沉降对津保高铁坡度变化具有影响作用,需控制地面沉降,保证高速铁路的安全运营。

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周超凡
宫辉力
陈蓓蓓
雷坤超
施轹原
赵宇
关键词 地面沉降津保高速铁路小波变换随机森林    
Abstract

Land subsidence is an environmental geological phenomenon caused by many factors, and it can reduce the smoothness of high-speed railways and thus affects the safe operation of high-speed railways. Traditional rardom forest models do not take account of the internal complexity of time series data in the prediction of time series data. Therefore, this paper constructs a wavelet transform-random forest (WT-RF) prediction model, predicts the land subsidence along the Tianjin-Baoding high-speed railway using the model, and assesses the impacts of land subsidence on the changes in the slope of the high-speed railway. The results are as follows: ① From 2016 to 2018, the change range of the slope of the Tianjin-Baoding high-speed railway was 0~0.16‰ due to the cumulative land subsidence. ② The WT-RF model showed high prediction accuracy of the land subsidence. ③ From 2018 to 2020, the land subsidence still showed an increasing trend, although the change range of the slope along the Tianjin-Baoding high-speed railway was 0~0.2 ‰. It can be concluded that the land subsidence has an impact on the changes in the slope of the Tianjin-Baoding high-speed railway. Therefore, it is necessary to control the land subsidence to ensure the safe operation of the high-speed railway.

Key wordsland subsidence    Tianjin-Baoding high-speed railway    wavelet transform    rardom forest
收稿日期: 2020-11-09      出版日期: 2021-12-23
ZTFLH:  TP79  
基金资助:国家自然科学基金重点项目“京津冀典型区地下空间演化与地面沉降响应机理研究”(41930109/D010702);国家自然科学基金面上项目“南水进京背景下地面沉降演化机理”(41771455/D010702);北京卓越青年科学家项目(BJJWZYJH01201910028032);北京市自然科学基金面上项目“新水情背景下京津高铁沿线地面沉降演化机制及调控方法”(8182013);北京市优秀人才培养资助青年拔尖个人项目
通讯作者: 宫辉力
作者简介: 周超凡(1990-),女,博士,研究方向为地理信息系统与遥感技术应用。Email: chaofan0322@126.com
引用本文:   
周超凡, 宫辉力, 陈蓓蓓, 雷坤超, 施轹原, 赵宇. 联合WT-RF的津保高铁沿线地面沉降预测[J]. 自然资源遥感, 2021, 33(4): 34-42.
ZHOU Chaofan, GONG Huili, CHEN Beibei, LEI Kunchao, SHI Liyuan, ZHAO Yu. Prediction of land subsidence along Tianjin-Baoding high-speed railway using WT-RF method. Remote Sensing for Natural Resources, 2021, 33(4): 34-42.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2020351      或      https://www.gtzyyg.com/CN/Y2021/V33/I4/34
Fig.1  津保高速铁路位置及Sentinel-1A数据空间分布示意图
雷达影像 Sentinel-1A(S1A)
轨道方向 升轨
分辨率/m 5×20
波段 C波段
极化方式 VV
数据模式 干涉宽幅模式(interferometric wide swath,IW)
重访周期/d 12
影像数量/景 140
时间范围 2016年1月14日—2019年12月24日
Tab.1  雷达影像信息
Fig.2  整体技术路线
Fig.3  2016—2018年京津冀平原典型区地面沉降空间分布特征
Fig.4  2016—2018年津保高速铁路沿线地面形变速率
Fig.5  2016—2018年津保高铁沿线坡度变化
Fig.6  地面沉降时间序列信息5级小波分解结果
参数名称 取值 参数名称 取值
子模型数 100 最大深度 10
不纯度指标 基尼系数 叶节点最小样本数 12
Tab.2  随机森林模型参数
模型 R2 RMSE/mm MAE/mm
ARIMA 0.61 33.29 17.00
随机森林 0.97 8.68 5.12
WT-RF 0.97 5.87 2.03
Tab.3  3种模型精度对比
Fig.7  基于WT-RF模型的津保高速铁路沿线地面沉降预测结果
形变差值/mm (10,20] (5,10] (1,5] [0,1]
研究点数/个 131 164 193 53
Tab.4  基于WT-RF模型预测的2019年形变结果与InSAR监测信息对比
Fig.8  2016—2019年和2016—2020年津保高铁沿线坡度变化
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