Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning

The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial vari...

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Bibliographic Details
Published in:Geosciences (Basel) 2023-05, Vol.13 (5), p.133
Main Authors: Fu, Guobin, Schmid, Wolfgang, Castellazzi, Pascal
Format: Article
Language:English
Subjects:
Altitude
Aquifers
Artificial intelligence
Artificial satellites in remote sensing
Clay soils
Correlation
Deformation
Deformations (Mechanics)
Drought
Dynamics
Earth science
Environmental aspects
Groundwater
groundwater dynamic
Groundwater levels
Groundwater management
Groundwater resources
InSAR data
Interferometric synthetic aperture radar
land subsidence
Land use
Learning algorithms
Lithology
Machine learning
Management
Moisture effects
Perturbation
Radar
Rain
random forest
Resource management
Risk assessment
Roads & highways
SAR (radar)
Soil moisture
Spatial data
Spatial variability
Spatial variations
Subsidences (Earth movements)
Synthetic aperture radar
Synthetic aperture radar interferometry
Topography
Variability
Vegetation
Water
Water resources
Water, Underground
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Summary:The interferometric synthetic aperture radar (InSAR) technique was used in this study to derive the temporal and spatial information of ground deformation and explore its temporal correlation with groundwater dynamics. The random forest (RF) machine learning method was used to model the spatial variability of the temporal correlation and understand its influential contributors. The results showed that groundwater dynamics appeared to be an important factor in InSAR deformation at some bores where strong and positive correlations were observed. The RF model could explain up to 72% of spatial variances between InSAR deformation and groundwater dynamics. The spatial and temporal InSAR coherence (a proxy for the noise in InSAR results that is strongly related to vegetation) and soil moisture (difference, trend, and amplitude) were the most important factors explaining the spatial pattern of the temporal correlation between InSAR displacements and groundwater levels. This result confirms that noise sources (including deformation model fitting errors and radar signal decorrelation) and perturbation of the InSAR signal related to vegetation and surficial soils (clay content, moisture changes) should be accounted for when interpreting InSAR to support groundwater-related risk assessments and in groundwater resource management activities.
ISSN:2076-3263
2076-3263
DOI:10.3390/geosciences13050133