Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City-A Case Study of Lanzhou, China
文献类型：期刊论文
| 作者 | Xu, Yuanmao4; Wu, Zhen4; Zhang, Huiwen3; Liu, Jie2; Jing, Zhaohua1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-05-30 |
| 卷号 | 15期号:11页码:32 |
| 关键词 | land subsidence InSAR machine learning Lanzhou risk assessment |
| DOI | 10.3390/rs15112851 |
| 通讯作者 | Zhang, Huiwen(zhanghuiwen@lzb.ac.cn) |
| 英文摘要 | As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a long time series helps us grasp the patterns of change in various types of ground hazard. In this paper, we present the results of using Interferometric Synthetic Aperture Radar (InSAR) to monitor land subsidence in the main urban area of Lanzhou from 26 October 2014 to 12 December 2021. The main influential factors leading to subsidence were analyzed and combined via machine learning simulation to assess the land subsidence risk grade distribution of a building unit. The results show that the annual average deformation rate in Lanzhou ranged from -18.74 to 12.78 mm/yr. Linear subsidence dominated most subsidence areas in Lanzhou during the monitoring period. The subsidence areas were mainly distributed along the Yellow River, the railway, and villages and towns on the edges of urban areas. The main areas where subsidence occurred were the eastern part of Chengguan District, the railway line in Anning District, and the southern parts of Xigu District and Qilihe urban area, accounting for 38.8, 43.5, 32.5, and 51.8% of the area of their respective administrative districts, respectively. The random forest model analysis results show that the factors influencing surface subsidence in Lanzhou were, in order of importance, precipitation, the distribution of faults, the lithology of strata, high-rise buildings, and the distance to the river and railway. Lanzhou experienced excessive groundwater drainage in some areas from 2015 to 2017, with a 1 m drop in groundwater and 14.61 mm surface subsidence in the most critical areas. At the same time, extensive subsidence occurred in areas with highly compressible loess ground and most railway sections, reaching a maximum of -11.68 mm/yr. More than half of the super-tall building areas also showed settlement funnels. The area at a very high risk of future subsidence in Lanzhou covers 22.02 km(2), while the high-subsidence-risk area covers 54.47 km(2). The areas at greatest risk of future subsidence are Chengguan District and Qilihe District. The city contains a total of 51,163 buildings in the very high-risk area, including about 44.57% of brick-and-timber houses, 51.36% of old housing, and 52.78% of super-tall buildings, which are at especially high risk of subsidence, threatening the lives and properties of the population. The deformation results reveal poor building safety in Lanzhou, providing an essential basis for future urban development and construction. |
| WOS关键词 | PERMANENT SCATTERERS ; BASIN |
| 资助项目 | Project of Gansu Significant Natural Science Foundation[21ZD4FA011] ; Project of Gansu Natural Science Foundation[22JR5RA824] ; Special Fund for Innovation Team, Gansu Earthquake Agency[2019TD-01-02] ; Science for Earthquake Resilience of the China Earthquake Administration[XH20059] ; Basic Scientific Research Foundation of the China Earthquake Administration[2020IESLZ04] ; National Natural Science Foundation of China[41761006] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001004850800001 |
| 资助机构 | Project of Gansu Significant Natural Science Foundation ; Project of Gansu Natural Science Foundation ; Special Fund for Innovation Team, Gansu Earthquake Agency ; Science for Earthquake Resilience of the China Earthquake Administration ; Basic Scientific Research Foundation of the China Earthquake Administration ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194813]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Huiwen |
| 作者单位 | 1.AECOM Consultants Co Ltd, China State Construction Engn Corp, Lanzhou 730000, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Gansu Desert Control Res Inst, State Key Lab Breeding Base Desertificat & Aeolian, Lanzhou 730070, Peoples R China 4.China Earthquake Adm, Lanzhou Inst Seismol, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Yuanmao,Wu, Zhen,Zhang, Huiwen,et al. Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City-A Case Study of Lanzhou, China[J]. REMOTE SENSING,2023,15(11):32. |
| APA | Xu, Yuanmao,Wu, Zhen,Zhang, Huiwen,Liu, Jie,&Jing, Zhaohua.(2023).Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City-A Case Study of Lanzhou, China.REMOTE SENSING,15(11),32. |
| MLA | Xu, Yuanmao,et al."Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City-A Case Study of Lanzhou, China".REMOTE SENSING 15.11(2023):32. |
入库方式： OAI收割
来源：地理科学与资源研究所
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