Arid
DOI10.1016/j.jhydrol.2022.127627
An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas
Khodaei, Behshid; Hashemi, Hossein; Naghibi, Seyed Amir
通讯作者Naghibi, SA
来源期刊JOURNAL OF HYDROLOGY
ISSN0022-1694
EISSN1879-2707
出版年2022
卷号608
英文摘要Land subsidence is an increasing human-induced disaster that not only damages building and transportation structures but also diminishes the water storage capacity of the aquifers. Land subsidence is a very complex phenomenon impacted by various geo-environmental and hydrological factors. Application of the interferometric synthetic aperture radar (InSAR) is becoming a common approach to detect land subsidence rates, though, it suffers from the lack of continuity over the spatial surfaces due to the vegetation decorrelation, coverage alterations (cultivation and non-cultivation seasons), in the agricultural areas, and rough topography. The lack of continuity can, however, be resolved using artificial intelligence. In our case study, while InSAR deformation data only covered ~ 2% of the plain's surface, we employed boosted regression trees (BRT) and extreme gradient boosting (XGB) algorithms to provide a full coverage map of the groundwater-induced land subsidence based on the InSAR analysis. For this, a set of topographical, hydrological, hydrogeological, and anthropogenic factors was selected. The InSAR and input factors' resolution data were resampled to a 100-by-100 m to match. The implemented models predicted the long-term deformation rate with the acceptable performances of the BRT (RMSE = 3.3 mm/year, MAE = 2.0 mm/year, R-2 = 0.985) and the XGB with linear booster (RMSE = 3.5 mm/ year, MAE = 2.1 mm/year, R-2 = 0.983). Considering the substantial ground deformation in the studied area (from-216 to 49 mm/year), RMSE values of 3.3, and 3.5 mm/year between the InSAR measurement and model predictions show great potential for combined InSAR-machine learning technique for pumping-driven land subsidence studies. Thus, the introduced approach is suggested for other areas being damaged by excessive pumping and agricultural development to produce an accurate full coverage map of subsidence.
英文关键词InSAR Machine learning Ground deformation Land subsidence Groundwater Hydrogeology
类型Article
语种英语
开放获取类型hybrid
收录类别SCI-E
WOS记录号WOS:000790506100001
WOS关键词LAND SUBSIDENCE ; SURFACE DEFORMATION ; METROPOLITAN-AREA ; PERFORMANCE ; PREDICTION ; REGRESSION ; ALGORITHM ; LANDSLIDE ; VALLEY ; GIS
WOS类目Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向Engineering ; Geology ; Water Resources
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/393478
推荐引用方式
GB/T 7714
Khodaei, Behshid,Hashemi, Hossein,Naghibi, Seyed Amir. An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas[J],2022,608.
APA Khodaei, Behshid,Hashemi, Hossein,&Naghibi, Seyed Amir.(2022).An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas.JOURNAL OF HYDROLOGY,608.
MLA Khodaei, Behshid,et al."An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas".JOURNAL OF HYDROLOGY 608(2022).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Khodaei, Behshid]的文章
[Hashemi, Hossein]的文章
[Naghibi, Seyed Amir]的文章
百度学术
百度学术中相似的文章
[Khodaei, Behshid]的文章
[Hashemi, Hossein]的文章
[Naghibi, Seyed Amir]的文章
必应学术
必应学术中相似的文章
[Khodaei, Behshid]的文章
[Hashemi, Hossein]的文章
[Naghibi, Seyed Amir]的文章
相关权益政策
暂无数据
收藏/分享

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。