Knowledge Resource Center for Ecological Environment in Arid Area
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
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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). |
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