必须声明标量变量 "@Script_ID"。 顾及降雨及库水位因素的滑坡时滞分析与预测——以三峡库区新铺滑坡为例-《地球科学与环境学报》
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[1]黄观文,王家兴,杜 源,等.顾及降雨及库水位因素的滑坡时滞分析与预测——以三峡库区新铺滑坡为例[J].地球科学与环境学报,2021,43(03):621-631.[doi:10.19814/j.jese.2021.01019]
 HUANG Guan-wen,WANG Jia-xing,DU Yuan,et al.Time-delay Analysis and Prediction of Landslide Considering Precipitation and Reservoir Water Level—A Case Study of Xinpu Landslide in Three Gorges Reservoir Area, China[J].Journal of Earth Sciences and Environment,2021,43(03):621-631.[doi:10.19814/j.jese.2021.01019]
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顾及降雨及库水位因素的滑坡时滞分析与预测——以三峡库区新铺滑坡为例(PDF)
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《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

卷:
第43卷
期数:
2021年第03期
页码:
621-631
栏目:
地球信息科学
出版日期:
2021-05-15

文章信息/Info

Title:
Time-delay Analysis and Prediction of Landslide Considering Precipitation and Reservoir Water Level—A Case Study of Xinpu Landslide in Three Gorges Reservoir Area, China
文章编号:
1672-6561(2021)03-0621-11
作者:
黄观文王家兴杜 源白正伟王 铎
(长安大学 地质工程与测绘学院,陕西 西安 710054)
Author(s):
HUANG Guan-wen WANG Jia-xing DU Yuan BAI Zheng-wei WANG Duo
(School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China)
关键词:
滑坡预测 库岸滑坡 互相关分析 时滞分析 时滞GM(13) 灰色模型 新铺滑坡 三峡库区
Keywords:
landslide prediction reservoir bank landslide cross correlation analysis time-delay analysis time-delay GM(13) gray model Xinpu landslide Three Gorges reservoir area
分类号:
P642; P228
DOI:
10.19814/j.jese.2021.01019
文献标志码:
A
摘要:
降雨及库水位涨落是引起库岸滑坡形变失稳的主要诱发因素,但滑坡位移速率对此类诱发因素的响应具有一定的滞后性,影响人类对滑坡所处运动状态的判断与预测。针对常规预测模型中未考虑时滞效应的问题,利用三峡库区新铺滑坡的GNSS位移监测数据、奉节气象站降雨数据以及三峡库区库水位涨落数据,通过对监测区内9个GNSS监测点的位移速率序列与降雨量、库水位高程序列进行时滞互相关分析,确定时滞参数,进而应用多变量灰色系统理论方法,建立了时滞GM(1,3)预测模型,并对滑坡位移速率进行预测验证。结果表明:三峡库区新铺滑坡位移速率与降雨量显著相关,对降雨量的响应滞后时间约为5 d,滑体中后部受降雨影响比前缘更明显; 位移速率与库水位高程高度相关,对三峡库区库水位涨落的响应滞后时间约为31 d,滑坡前缘受库水位涨落影响更明显,且离长江越近,滞后时间越短; 利用加入时滞参数的时滞GM(1,3)模型进行预测,模型拟合优度达到0.702,相比GM(1,1)模型和未顾及时滞因素的GM(1,3)模型,预测精度分别提升了53.8%和58.3%,平均绝对误差百分比分别降低了7.19%和7.47%,在滑坡位移速率预测及库岸滑坡防灾减灾领域具有一定的工程应用价值。
Abstract:
Rainfall and reservoir water level fluctuation are the main inducing factors of landslide deformation and instability, but the response of landslide displacement rate to such inducing factors is lagging, and affects the judgment and prediction of the movement state of landslide. In order to solve the problem that time-delay effect is not considered in the conventional prediction model, the GNSS displacement monitoring data of Xinpu landslide in Three Gorges reservoir area, rainfall data of Fengjie meteorological station and reservoir water level fluctuation data in Three Gorges reservoir area were used; through the time-delay cross correlation analysis of displacement rate, precipitation and reservoir water level elevation series of 9 GNSS points in the monitoring area, the time-delay parameters were determined; by using the multi-variable grey system theory, the time-delay GM(1,3)model was established to predict and verify the landslide displacement rate. The results show that the displacement rate of Xinpu landslide in Three Gorges reservoir area is significantly correlated with precipitation, and the response lag time to precipitation is about 5 days, the middle and rear parts of landslide are more affected by precipitation than the front edge, and the displacement rate is highly correlated with the reservoir water level elevation; the response lag time to reservoir water level fluctuation in Three Gorges reservoir area is about 31 days, the front edge of landslide is more obviously affected by the reservoir water level fluctuation, and the closer to Yangtze River, the shorter the lag time; using the time-delay GM(1,3)model, the goodness of fit of the model reaches 0.702; compared with GM(1,1)model and GM(1,3)model without time-delay factors, the prediction accuracy of time-delay GM(1,3)model is improved respectively 53.8% and 58.3%, and the mean absolute percentage error reduces by 7.19% and 7.47%, respectively. The time-delay GM(1,3)model has a certain engineering application value in the field of landslide displacement rate prediction and reservoir bank landslide disaster prevention and reduction.

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备注/Memo

备注/Memo:
收稿日期:2021-01-19; 修回日期:2021-03-26
基金项目:国家重点研发计划项目(2018YFC1505105,2019YFC1509802); 国家自然科学基金项目(41731066,41941019)
作者简介:黄观文(1983-),男,江苏淮安人,教授,博士研究生导师,工学博士,E-mail:huang830928@163.com。
更新日期/Last Update: 2021-05-01