An Integrated InSAR-Machine Learning Approach for Ground Deformation Rate Modeling in Arid Areas
(2022) In Journal of Hydrology 608.- Abstract
- 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... (More)
- 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, R2 = 0.985) and the XGB with linear booster (RMSE = 3.5 mm/year, MAE = 2.1 mm/year, R2 = 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.
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Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3a52a0a3-05dd-4e35-8e69-e252945b2af4
- author
- Naghibi, Seyed Amir LU ; Khodaei, Behshid LU and Hashemi, Hossein LU
- organization
- publishing date
- 2022-02-18
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Hydrology
- volume
- 608
- publisher
- Elsevier
- external identifiers
-
- scopus:85125145736
- ISSN
- 0022-1694
- DOI
- 10.1016/j.jhydrol.2022.127627
- language
- English
- LU publication?
- yes
- id
- 3a52a0a3-05dd-4e35-8e69-e252945b2af4
- date added to LUP
- 2022-03-15 08:28:59
- date last changed
- 2023-10-09 01:27:12
@article{3a52a0a3-05dd-4e35-8e69-e252945b2af4, abstract = {{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, R2 = 0.985) and the XGB with linear booster (RMSE = 3.5 mm/year, MAE = 2.1 mm/year, R2 = 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.<br/><br/>}}, author = {{Naghibi, Seyed Amir and Khodaei, Behshid and Hashemi, Hossein}}, issn = {{0022-1694}}, language = {{eng}}, month = {{02}}, publisher = {{Elsevier}}, series = {{Journal of Hydrology}}, title = {{An Integrated InSAR-Machine Learning Approach for Ground Deformation Rate Modeling in Arid Areas}}, url = {{http://dx.doi.org/10.1016/j.jhydrol.2022.127627}}, doi = {{10.1016/j.jhydrol.2022.127627}}, volume = {{608}}, year = {{2022}}, }