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

Fu, Guobin; Schmid, Wolfgang; Castellazzi, Pascal

doi:10.3390/geosciences13050133

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…To identify flood-prone areas along the Kashkan River, we employed the RF algorithm implemented in the R programming language. RF is an innovative ensemble method consisting of classification and/or regression trees, which relies on the bootstrapping subset selection technique [37]. In this study, the choice of RF over other machine The climate in the study area leads to hot summers and cold winters, with an average annual precipitation ranging from 400 to 900 mm.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

Ghahraman,

Nagy,

Nooshin Nokhandan

2023

Geosciences

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We utilized the random forest (RF) machine learning algorithm, along with nine topographical/morphological factors, namely aspect, slope, geomorphons, plan curvature, profile curvature, terrain roughness index, surface texture, topographic wetness index (TWI), and elevation. Our objective was to identify flood-prone areas along the meandering Kashkan River and investigate the role of topography in riverbank inundation. To validate the flood susceptibility map generated by the random forest algorithm, we employed Sentinel-1 GRDH SAR imagery from the March 2019 flooding event in the Kashkan river. The SNAP software and the OTSU thresholding method were utilized to extract the flooded/inundated areas from the SAR imagery. The results showed that the random forest model accurately pinpointed areas with a “very high” and “high” risk of flooding. Through analysis of the cross-sections and SAR-based flood maps, we discovered that the topographical confinement of the meander played a crucial role in the extent of inundation along the meandering path. Moreover, the findings indicated that the inner banks along the Kashkan river were more prone to flooding compared to the outer banks.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

Ghahraman,

Nagy,

Nooshin Nokhandan

2023

Geosciences

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“…For instance, Chen et al [21] utilized RF to explore the relationship between land subsidence and groundwater in different aquifers in Beijing Plain during 2011-2018, which indicated groundwater in the second confined aquifer had the biggest impact on the subsidence of all aquifers. Fu et al [22] applied RF to investigate the spatial distribution of temporal correlation, and it could explain 72% and 60% of the spatial variance between the ground deformation and critical head/groundwater level, respectively.…”

Section: Introductionmentioning

confidence: 99%

Integrating SBAS-InSAR and Random Forest for Identifying and Controlling Land Subsidence and Uplift in a Multi-Layered Porous System of North China Plain

Wang,

Chen,

Wang

et al. 2024

Remote Sensing

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Controlling groundwater table decline could mitigate land subsidence and induced environmental hazards in over-explored areas. Nevertheless, this becomes a challenge in the multi-layered porous system as (in)elastic deformation simultaneously occurs due to vast spatiotemporal variability in the groundwater table. In this study, SBAS-InSAR was used to estimate annual land deformation during 2017–2022 in a specific region of North China Plain, in which aquifers are composed of many layers of fine-grained compressible sediments and the groundwater table has experienced a prolonged decline. The random forest (RF) was applied to establish the nonlinear relationship between accumulated deformation and its potential driving factors, including the depth to the groundwater table (GWD) and its change rate, and the compressible sediment thickness. Results show that the marked subsidence and uplift co-exist in the region even though the groundwater table has risen widely since the South–North Water Diversion Project. The land subsidence is attributed to inelastic compaction of the thick compressible deposits in depression cone centers, where the GWD is over 40 m and 90 m in the shallow and deep aquifers, respectively. In contrast, the marked uplift is primarily attributed to fast rising of the groundwater table (e.g., −2.44 m/a). The RF predictions suggest that, to control the subsidence, the GWD should be less than 20 and 70 m in the shallow and deep aquifers, respectively, and the rising rate of the GWD should increase to 2–5 times of current rates in the depression cones. To mitigate the marked uplift, the rising rate of the GWD should reduce to 1/2–1/5 of the current rates in the shallow aquifers. The uneven deformations of sediments in the depression cone centers and uplift in their boundaries may exacerbate geohazards. Therefore, it is vital to implement appropriate governance of groundwater recovery in the multi-layered porous system.

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“…A similar task for dam deformations is addressed in [36,37], but using neural network simulation. Specifically, the question of urban land subsidence simulation, especially due to subway construction, is considered in the papers [42][43][44][45][46][47][48][49][50][51][52][53][54][55]. These works investigate interferometric SAR as an observation method.…”

Section: Introductionmentioning

confidence: 99%

“…Paper [52] considers a random forest machine learning algorithm to classify the factors that majorly affect land subsidence. A similar study with a random forest model was undertaken in [54]. Despite the correct approach, the papers do not deal with subsidence prediction.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Overpass Displacements Due to Subway Construction Land Subsidence Using Machine Learning

Shults,

Bilous,

Ormambekova

et al. 2023

Urban Science

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Modern cities are full of complex and substantial engineering structures that differ by their geometry, sizes, operating conditions, and technologies used in their construction. During the engineering structures’ life cycle, they experience the effects of construction, environmental, and functional loads. Among those structures are bridges and road overpasses. The primary reason for these structures’ displacements is land subsidence. The paper addresses a particular case of geospatial monitoring of a road overpass that is affected by external loads invoked by the construction of a new subway line. The study examines the methods of machine learning data analysis and prediction for geospatial monitoring data. The monitoring data were gathered in automatic mode using a robotic total station with a frequency of 30 min, and were averaged daily. Regression analysis and neural network regression with machine learning have been tested on geospatial monitoring data. Apart from the determined spatial displacements, additional parameters were used. These parameters were the position of the tunnel boring machines, precipitation level, temperature variation, and subsidence coefficient. The primary output of the study is a set of prediction models for displacements of the overpass, and the developed recommendations for correctly choosing the prediction model and a set of parameters and hyperparameters. The suggested models and recommendations should be considered an indispensable part of geotechnical monitoring for modern cities.

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

Cited by 6 publications

References 29 publications

Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

Integrating SBAS-InSAR and Random Forest for Identifying and Controlling Land Subsidence and Uplift in a Multi-Layered Porous System of North China Plain

Analysis of Overpass Displacements Due to Subway Construction Land Subsidence Using Machine Learning

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