An Integrated InSAR-Machine Learning Approach for Ground Deformation Rate Modeling in Arid Areas
Naghibi, Seyed Amir LU ; Khodaei, Behshid LU
and Hashemi, Hossein
LU
(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.
(Less)
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}},
}