干旱区生态环境知识资源中心(Arid): An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas

Arid

DOI	10.1016/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
ISSN	0022-1694
EISSN	1879-2707
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).