State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal

Machine learning-based techniques for land subsidence simulation in an urban area

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Land subsidence prediction in Zhengzhou's main urban area using the GTWR and LSTM models combined with the Attention Mechanism

In recent years, due to urbanization and human activities, groundwater overexploitation has become increasingly severe, resulting in some degrees of land subsidence and, consequently, causing a series of geological disasters and other environmental issues. Therefore, large-scale and high-precision land subsidence prediction is of great importance for the prevention and control of geological disasters. However, the existing prediction models and methods ignore the effects of the spatiotemporal non-stationary relationships between the influencing factors and the accumulated land subsidence, causing the poor accuracy of the predicted land subsidence results. In this context, a Geographically and Temporally Weighted Regression combined with the Long Short-Term Memory (LSTM)-multivariable and Attention Mechanism (AM) (GTWR-LSTMm-AM) was proposed to more accurately predict the deformation of time series land subsidence in this study. The small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) was used to reveal the temporal deformation information of Zhengzhou's main urban area, then the GTWR model was used to assess the spatiotemporal non-stationarity relationships between the accumulated land subsidence and its influencing factors monthly groundwater stability level, monthly precipitation and Normalized Difference Vegetation Index (NDVI) data, and to determine the corresponding weight matrix. In addition, we introduced an LSTM model with AM to extract key information from the time-series land subsidence data and adjusted the dynamic weights of the three selected influencing factors to predict the land subsidence in Zhengzhou's main urban area. The prediction accuracy R2 of the GTWR-LSTMm-AM model reaches 0.972, which is higher than 0.929 of the LSTMm model. The prediction accuracy RMSE is less than 3 mm and reaches 2.403 mm. In addition, we determined the importance of the impact factor on the subsidence results by randomly interrupting the impact factor time series, disclosuring that the monthly groundwater level contributed the most to the land subsidence in Zhengzhou's main urban area.

Reconstructing missing time-varying land subsidence data using back propagation neural network with principal component analysis

Land subsidence, a complex geophysical phenomenon, necessitates comprehensive time-varying data to understand regional subsidence patterns over time. This article focuses on the crucial task of reconstructing missing time-varying land subsidence data in the Choshui Delta, Taiwan. We propose a novel algorithm that leverages a multi-factorial perspective to accurately reconstruct the missing time-varying land subsidence data. By considering eight influential factors, our method seeks to capture the intricate interplay among these variables in the land subsidence process. Utilizing Principal Component Analysis (PCA), we ascertain the significance of these influencing factors and their principal components in relation to land subsidence. To reconstruct the absent time-dependent land subsidence data using PCA-derived principal components, we employ the backpropagation neural network. We illustrate the approach using data from three multi-layer compaction monitoring wells from 2008 to 2021 in a highly subsiding region within the study area. The proposed model is validated, and the resulting network is used to reconstruct the missing time-varying subsidence data. The accuracy of the reconstructed data is evaluated using metrics such as root mean square error and coefficient of determination. The results demonstrate the high accuracy of the proposed neural network model, which obviates the need for a sophisticated hydrogeological numerical model involving corresponding soil compaction parameters.

Hydrogeological characteristics and water chemistry in a coastal aquifer of Korea: implications for land subsidence

Land subsidence is the gradual or sudden dropping of the ground surface developed by increasing the total stress. Most studies have discussed the relationship between land subsidence with groundwater level. However, there is a lack of discussion on groundwater environmental changes after occurring land subsidence. This study aimed to evaluate the hydrogeological and water chemistry characteristics of construction sites with land subsidence. Land subsidence in the Yangyang coastal area occurred suddenly on August 3, 2022, when the retaining wall of the construction collapsed. The groundwater level was measured three times, and water samples were collected twice between August 5, 2022, and September 5, 2022, for laboratory analysis. After land subsidence occurred, the average groundwater level was - 19.91 m ground level (GL) on August 9, 2022, and finally decreased to - 19.21 m GL on September 05, 2022. The groundwater levels surrounding the construction site gradually increased for a month. The electrical conductivity value measured at the monitoring wells ranged from 89 to 7800 μS/cm, and four wells exceeded the measurement limit near the groundwater leaked points. The highest mixing ratio of leaked water samples, collected on August 9, 2022, was 27.6%. Furthermore, the fresh groundwater-saltwater interface depth was estimated to be above the construction bottom. Although groundwater levels recovered, the groundwater quality continuously is affected by saltwater. This finding could contribute to understanding the hydrogeological characteristics surrounding construction sites with land subsidence and provide insight into the hydrochemical evolution process during declined groundwater levels in coastal aquifers.

New Preconsolidation Heads Following the Long-Term Hydraulic-Head Decline and Recovery in Houston, Texas

The Houston area in Texas, United States, has been experiencing land subsidence for a century from the 1920s to 2010s. A substantial portion of the Houston area had finished a consolidation cycle following the long-term hydraulic head decline and recovery. A new "maximum effective stress" (preconsolidation stress) was preserved in the memory of the aquitards. For an aquifer system comprising aquifers and aquitards, the preconsolidation stress is corresponding to the lowest hydraulic head in the aquitards, not in the aquifers. Preconsolidation head is generally regarded as a groundwater-level threshold below which inelastic compaction begins. The preconsolidation head finalized after the long-term hydraulic head decline and recovery is called new preconsolidation head. This study has developed an empirical equation for projecting the new preconsolidation head. According to this study, the new preconsolidation heads in the primary aquifers (lower Chicot and Evangeline) are local specific: varying from about 30 m below land surface (-30 m) in the south to -50 m in the north of the Harris-Galveston Subsidence District (HGSD) Regulatory Area 1, from -60 m in the east to -80 m in the west of Area 2, and from -70 m in the south to -100 m in the center of Area 3. In Areas 1 and 2, the current hydraulic heads are about 10 m to 20 m higher than the local new preconsolidation heads; thus, remarkable land subsidence (>1 cm/year) would not be reinitiated unless the hydraulic heads are to fall below the local new preconsolidation head.

Use of InSAR data for measuring land subsidence induced by groundwater withdrawal and climate change in Ardabil Plain, Iran

The Ardabil plain, with an approximate area of 1097.2 km² in northwestern Iran, has experienced land subsidence due to intensive groundwater withdrawal and long seasons of drought in recent years. Different techniques have been used to investigate and evaluate subsidence in this region including: Global Positioning Systems (GPS), Levelling, and Geotechnical methods. These methods are typically expensive, time-consuming, and identify only a small fraction of the areas prone to subsidence. This study employs an Interferometric Synthetic Aperture Radar (InSAR) technique to measure the long-term subsidence of the plain. An open-source SAR interferometry time series analysis package, LiCSBAS, that integrates with the automated Sentinel-1 InSAR processor (COMET-LiCSAR) is used to analyze Sentinel-1 satellite images from October 2014 to January 2021. Processing of Sentinel-1 images shows that the Ardabil plain has been facing rapid subsidence due to groundwater pumping and reduced rainfall, especially between May 2018 to January 2019. The maximum subsidence rate was 45 mm/yr, measured at the southeastern part of the plain. While providing significant advantages (less processing time and disk space) over other InSAR processing packages, implementation of the LiCSBAS processing package and its accuracy for land subsidence measurements at different scales needs further evaluation. This study provides a procedure for evaluating its efficiency and accuracy for land subsidence measurements by comparing its measurements with the results of the GMTSAR and geotechnical numerical modeling. The results of geotechnical numerical modeling showed land subsidence with an average annual rate of 38 mm between 2006 and 2020, which was close to measurements using the InSAR technique. Comparison of the subsidence measurements of the Ardabil plain using the LiCSBAS package with results obtained from other techniques shows that LiCSBAS is able to accurately detect land deformation at large scales (~ km). However, they may not be optimized for more local deformations such as infrastructure monitoring.

Different Ground Subsidence Contributions Revealed by Integrated Discussion of Sentinel-1 Datasets, Well Discharge, Stratigraphical and Geomorphological Data: The Case of the Gioia Tauro Coastal Plain (Southern Italy)

Groundwater is the main water supply for agricultural and industrial needs in many coastal plains worldwide. Groundwater depletion often triggers land subsidence, which threatens manmade infrastructure and activities and aggravates other geohazards. We applied a multi-temporal interferometric synthetic aperture radar technique to Sentinel-1 datasets to detect ground motion in the Gioia Tauro plain (Calabria, Southern Italy) from 2018 to 2021. The InSAR data were analysed through the integrated use of groundwater head, stratigraphical and geomorphological data, and land use information to distinguish the potential subsidence divers. The results show that subsiding areas, with a mean rate of about 10 mm/yr, are in the middle of the plain, and their location is influenced by the spatial distribution of compressible sediments included in the shallow aquifer. Furthermore, the subsidence arrangement is spatially accordant with the main groundwater depression area, which can be ascribed to the ongoing and increasing water pumping for predominantly agricultural usage. We also observed that subsidence (up to 10 mm/yr) affects the western dock of the Gioia Tauro harbour, in front of which, in very shallow water, are two submarine canyon heads already affected by slides in the past.

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR

The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Land subsidence caused by groundwater withdrawal induced by mining is a relatively unknown phenomenon. This is primarily due to the small scale of such movements compared to the land subsidence caused by deposit extraction. Nonetheless, the environmental impact of drainage-related land subsidence remains underestimated. The research was carried out in the “Bogdanka” coal mine in Poland. First, the historical impact of mining on land subsidence and groundwater head changes was investigated. The outcomes of these studies were used to construct the influence method model. With field data, our model was successfully calibrated and validated. Finally, it was used for land subsidence estimation for 2030. As per the findings, the field of mining exploitation has the greatest land subsidence. In 2014, the maximum value of the phenomenon was 0.313 cm. However, this value will reach 0.364 m by 2030. The spatial extent of land subsidence caused by mining-induced drainage extends up to 20 km beyond the mining area’s boundaries. The presented model provided land subsidence patterns without the need for a complex numerical subsidence model. As a result, the method presented can be effectively used for land subsidence regulation plans considering the impact of mining on the aquifer system.

Monitoring of land surface subsidence using persistent scatterer interferometry techniques and ground truth data in arid and semi-arid regions, the case of Remah, UAE

The United Arab Emirates (UAE) is located in an arid desert climate with very limited water resources and scarce rainfall. Along with the fast development of the country, the water demand for agriculture, industrial, and domestic purposes increased and led to diminishing groundwater resources. In this study, we explore the land surface deformations due to groundwater overexploitation in the agricultural area of Remah by analyzing Sentinel-1 data between 2015 and 2019 with the novel Parallelized-Persistent Scatterer Interferometry (P-PSI) technique. The detected land surface deformations have been correlated to the recorded groundwater levels at nearby water wells. This study detected land surface deformations in a form of an extensive subsidence bowl (with 28.5 km in diameter) with a maximum subsidence rate of 40 mm/year and a standard deviation within the bowl of less than 2 mm/year. The detected subsidence was associated with a 12 m drop in the water table level within the study area. The Persistent Scatterers with the highest deformations rate were spatially correlated with the depression cone of the groundwater level. These findings provide useful insights in understanding the groundwater regime of the area and have an important role in assessing regional hazards and driving mitigation measures towards managing uncontrolled groundwater overexploitation for sustainable management of groundwater resources.

Decades of Ground Deformation in the Weihe Graben, Shaanxi Province, China, in Response to Various Land Processes, Observed by Radar Interferometry and Levelling

Ground deformation is usually used as direct evidence for early warning of geological hazards. The Weihe Graben, located in the southern margin of the Ordos Plateau, is surrounded by many active faults. Earthquakes (e.g., the 1556 Huaxian M 8 earthquake), mine accidents and ground fissures are the major hazards that pose great threats to this densely populated region. In order to characterise both tectonic and anthropogenic activities in the Weihe Graben, we use Envisat data from 2003 to 2010 and Sentinel-1 data from 2014 to 2021, combined with levelling data from 1970 to 2014, to investigate the long-term ground deformation. We generate four InSAR rate maps using the small-baseline subset (SBAS) algorithm. The uncertainties of the InSAR rates are 1–2 mm/year by calculating the differences between the InSAR and levelling measurements. From the deformation time series, we found that most of the faults surrounding the Weihe Graben move at a relatively slow rate (<3 mm/year). Elastic dislocation modelling based on the InSAR and levelling data yields a slip rate of 2.3 ± 0.3 mm/year for the Huashan Fault, the seismogenic fault for the 1556 Huaxian earthquake. Anthropogenic deformation is much stronger than the tectonic deformation. We identified localised subsidence of 12 mines with a deformation rate ranging from 5 to 17 mm/year. The cities of Xi’an and Xianyang also show evident subsidence, which is likely to be caused by groundwater extraction. Land subsidence in Xi’an has slowed down from an average rate of 10–20 mm/year between 2003 and 2010 to about 5–10 mm/year between 2017 and 2020, but in Xianyang, subsidence has increased dramatically in the past five years from 1 mm/year to 7 mm/year. This is because new industrial and urban development centres have gradually moved from Xi’an to Xianyang. We identified a region bounded by the Kouzhen-Guanshan and Fufeng-Liquan Faults with strong subsidence, as a result of excessive extraction of groundwater. To quantify the effects of crustal groundwater unloading on faults, we calculated the static Coulomb stress changes on the two faults and found that Coulomb stress changes are localised in the upper 5 km with a magnitude of 0.01–0.02 bar/year. The Coulomb stress changes might be large enough (0.1 bar) to affect local seismicity if such excessive extraction of groundwater continued for 10 years.

Adjacent-Track InSAR Processing for Large-Scale Land Subsidence Monitoring in the Hebei Plain

Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km2). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls.

Land Surface Subsidence Due to Mining-Induced Tremors in the Upper Silesian Coal Basin (Poland)—Case Study

Seismic phenomena threaten land-based buildings, structures, and infrastructure and can transform land topography. There are two basic types of seismic phenomena, namely, tectonic and anthropogenic, which differ mainly in epicenter depth, surface impact range, and magnitude (energy). This article shows how a land surface was changed by a series of seven rock mass tremors of magnitude ML = 2.3–2.6 in March–May 2017. Their immediate cause was the “momentary” acceleration of void clamping, which was activated by local and short-term seismic phenomena caused by human activity. The induced seismic events resulted from the geological structure of the rock mass, which in the specific region of examination was classified as being highly prone to mining tremors. The authors focused on describing vertical surface displacements in the Upper Silesian Coal Basin in the south of Poland. The surface deformations were identified using DInSAR technology, which allows quasi-continuous monitoring of large areas of land surface. The present research used freely available data from the Copernicus Program and seismic data from the European Plate Observing System.

Assessment of the Impact of the Spatial Extent of Land Subsidence and Aquifer System Drainage Induced by Underground Mining

The environmental impact assessment of underground mining usually includes the direct effects of exploitation. These are damage to rock mass and land subsidence. Continuous dewatering of the aquifer system is, however, necessary to carry out underground mining operations. Consequently, the drainage of the aquifer system is observed at a regional scale. The spatial extent of the phenomenon is typically much wider than the direct impact of the exploitation. The research presented was, therefore, aimed at evaluating both the direct and the indirect effects of underground mining. Firstly, the spatial extent of land subsidence was determined based on the Knothe theory. Secondly, underground mining-induced drainage of the aquifers was modeled. The 3D finite-difference hydrogeological model was constructed based on the conventional groundwater flow theory. The values of model hydrogeological parameters were determined based on literature and empirical data. These data were also used for model calibration. Finally, the results of the calculations were compared successfully with the field data. The research results presented indicate that underground mining’s indirect effects cover a much larger area than direct effects. Thus, underground mining requires a broader environmental assessment. Our results can, therefore, pave the way for more efficient management of groundwater considering underground mining.