Search Results (118)

Globally, intensive groundwater extraction has led to widespread land subsidence, posing severe threats to urban infrastructure, structural safety, and flood control capacity, and resulting in substantial economic losses and ecological degradation. Based on dynamic monitoring data and a poroelastic fluid–solid coupling model developed using COMSOL Multiphysics 6.2, this study systematically investigates the characteristics and evolution of land subsidence in Decheng District before and after the implementation of a groundwater extraction ban. Furthermore, recommendations and strategies for the sustainable management of regional groundwater resources are proposed. The results indicate that after the ban was enforced in 2020, the extraction volumes of deep and shallow groundwater in Decheng District decreased from 830,000 m³/a and 33,070,000 m³/a to 178,000 m³/a and 20,775,000 m³/a, respectively. The ban significantly influenced groundwater levels, with the recovery rate of deep groundwater increasing markedly from approximately 0.5 m/a before the ban to about 5 m/a afterward. Groundwater levels directly govern the rate of land subsidence; their decline increases the effective stress within the strata, leading to aquifer compaction and subsequent subsidence. Following the ban, the subsidence rate in Decheng District decreased significantly, with the annual subsidence volume reduced by more than 80% compared to the pre-ban period. Predictive analysis using the fluid–solid coupling model reveals that extraction from deep confined aquifers is the main driver of regional subsidence, with a time lag of approximately five years between groundwater level changes and subsidence response. After the implementation of the extraction ban, the subsidence rate slowed considerably. Over the long term, the subsiding strata tend to stabilize, although most of the subsidence that has already occurred is irreversible, making it difficult for the strata to return to their original state. In summary, the groundwater extraction ban has effectively facilitated groundwater recovery and mitigated land subsidence in Decheng District, though the response exhibits both temporal lag and spatial variability. Future work should focus on establishing an integrated monitoring and regulation system for land subsidence and groundwater dynamics to ensure the coordinated security of both water resources and the geological environment. These findings provide a scientific basis for informing land subsidence prevention and guiding the rational exploitation of groundwater resources in Decheng District. Full article

The latest climate change predictions indicate that the sea level will accelerate in the coming decades as a direct consequence of global warming. This is expected to seriously threaten low-lying coastal areas worldwide, resulting in severe coastal flooding with significant socio-economic impacts, leading to the loss of coastal settlements, exploitable land, and natural ecosystems. The main objective of this study is to provide a first-order preliminary estimation of potential inundation extents along the northern coastline of the Amvrakikos Gulf, a deltaic complex formed by the Arachthos, Louros, and Vouvos rivers in Western Greece, resulting from long-term sea-level rise induced by climate change, using the integrated Bathtub and Hydraulic Connectivity (HC) inundation method. A 2 m resolution Digital Elevation Model (DEM) was used, along with local long-term sea-level projections, for the years 2050 and 2100. Additionally, subsidence rates due to the compaction of deltaic sediments were taken into account. To assess the area’s proneness to inundation caused or enhanced by sea-level rise, the extent of each land cover type, the Natura 2000 Network protected area, the settlements, the total length of the road network, and the cultural assets located within the inundation zones under each climate change scenario were considered. The analysis revealed that under the optimistic SSP1-1.9 scenario of the Intergovernmental Panel on Climate Change (IPCC), areas of 40.81 km² (min 20.34 km², max 63.55 km²) and 69.10 km² (min 41.75 km², max 88.02 km²) could potentially be inundated by 2050 and 2100, respectively. Under the pessimistic SSP5-8.5 scenario, the inundation zone expands to 42.56 km² (min 37.05 km², max 66.31 km²) by 2050 and 84.55 km² (min 67.54 km², max 116.86 km²) by 2100, affecting a significant portion of ecologically valuable wetlands and water bodies within the Natura 2000 protected area. Specifically, the inundated Natura 2000 area is projected to range from 37.77 km² (min 20.30 km², max 46.82 km²) by 2050 to 50.74 km² (min 38.71 km², max 62.84 km²) by 2100 under the SSP1-1.9 scenario, and from 39.34 km² (min 34.53 km², max 49.09 km²) by 2050 to 60.48 km² (min 49.73 km², max 82.5 km²) by 2100 under the SSP5-8.5 scenario. Four settlements with a total population of approximately 800 people, as well as 32 economic facilities most of which operate in the secondary and tertiary sectors and are small to medium-sized economic units, such as olive mills, farms, gas stations, spare parts stores, construction companies, and food service establishments, are expected to experience significant exposure to coastal flooding and operational disruptions in the near future due to sea-level rise. Full article

The Chesapeake Bay (CB) region faces significant risks from relative sea-level change (RSLC), driven by global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS). This study introduces a methodology to decipher RSLC trends in the CB area by integrating these components. We develop trend equations spanning 1900–2100, incorporating acceleration for GMSLR and RSLR since 1992, with linear LS estimation using tide gauge, satellite altimetry, and InSAR data. Our approach employs dynamic RSLC equations, Maclaurin series expansions, and inverse simulations to project RSLC trends through 2100. Stable RSLC rates require over 122 years of data for reliable linear trend estimation, with the Baltimore tide gauge providing the necessary long-term dataset. Similarity in monthly mean sea-level variations within a coastal region enables a new method to identify LS from short-term tide gauge data by correlating it with corresponding long-term data at Baltimore. LS is categorized into bedrock-surface subsidence (BSS) and compaction subsidence (CS), with methods proposed to map BSS contours and estimate CS. CS is further classified into primary consolidation, secondary consolidation, construction-induced, and negative subsidence to determine specific compaction types. The projection model highlights the dominant influence of GMSLR acceleration since 1992, with local LS and RSLR influenced by ocean circulation, density changes, and gravitational, rotational, and deformational (GRD) effects. This integrated approach enhances understanding and predictive reliability for RSLC trends, supporting resilience planning and infrastructure adaptation in coastal CB communities. Full article

As one of the world’s primary energy sources, coal has driven economic development but has also led to severe surface subsidence. Currently, many regions around the world face significant ground deformation risks due to ongoing or legacy mining activities. Accurate monitoring and trend prediction are critical for enhancing subsidence early-warning capabilities and urban resilience. The northern region of Huainan City exhibits a spatial pattern characterized by the coexistence of mining areas, urban areas, and decommissioned mining sites, among which the mining areas show more pronounced surface deformation due to prolonged mining activities. To fully understand the subsidence evolution characteristics and differences across various regions, an LSTM–Transformer prediction model was constructed based on SBAS-InSAR monitoring technology to predict the surface subsidence processes in the three types of areas separately. The results indicated that the subsidence rate and cumulative subsidence in the mining areas were significantly greater than those in the urban and decommissioned areas, demonstrating more intense deformation activity. The average subsidence rates for the mining areas, urban areas, and decommissioned mining sites were −57.42 mm/yr, −5.37 mm/yr, and −3.21 mm/yr, respectively. The model’s prediction results demonstrated good accuracy across different regions, with the root mean square errors (RMSEs) for the mining areas, urban areas, and decommissioned mining sites being 2.16 mm, 1.03 mm, and 0.22 mm, respectively. The study shows that the constructed LSTM–Transformer hybrid model not only possesses strong capability in fitting subsidence trends but will also provide a scientific basis for future monitoring and early warning of surface subsidence hazards. Full article

The precise prediction of high-speed railway bridge pier settlement plays a crucial role in construction, maintenance, and long-term operation; however, current mainstream prediction methods mostly rely on independent analyses based on traditional or hybrid models, neglecting the impact of geological and environmental factors on subsidence. To address this issue, this paper proposes a multi-factor settlement prediction model for high-speed railway bridge piers named the Reversible Instance Normalization Multi-Scale Adaptive Resolution Stream CMamba, abbreviated as RMCMamba. During the data preprocessing process, the Enhanced PS-InSAR technology is adopted to obtain the time series data of land settlement in the study region. Utilizing the cubic improved Hermite interpolation method to fill the missing values of monitoring and considering the environmental parameters such as groundwater level, temperature, precipitation, etc., a multi-factor high-speed railway bridge pier settlement dataset is constructed. RMCMamba fuses the reversible instance normalization (RevIN) and the multiresolution forecasting head (MARSHead), enhancing the model’s long-range dependence capture capability and solving the time series data distribution drift problem. Experimental results demonstrate that in the multi-factor prediction scenario, RMCMamba achieves an MAE of 0.049 mm and an RMSE of 0.077 mm; in the single-factor prediction scenario, the proposed method reduces errors compared to traditional prediction approaches and other deep learning-based methods, with MAE values improving by 4.8% and 4.4% over the suboptimal method in multi-factor and single-factor scenarios, respectively. Ablation experiments further verify the collaborative advantages of combining reversible instance normalization and the multi-resolution forecasting head, as RMCMamba’s MAE values improve by 5.8% and 4.4% compared to the original model in multi-factor and single-factor scenarios. Hence, the proposed method effectively enhances the prediction accuracy of high-speed railway bridge pier settlement, and the constructed multi-source data fusion framework, along with the model improvement strategy, provides technological and experiential references for relevant fields. Full article

Interferometric Synthetic Aperture Radar (InSAR) technology is crucial for large-scale land subsidence analysis in cultivated areas within hilly and mountainous regions. Accurate prediction of this subsidence is of significant importance for agricultural resource management and planning. Addressing the limitations of existing subsidence prediction methods in terms of accuracy and model selection, this paper proposes a deep neural network prediction model based on Variational Mode Decomposition (VMD) and the Snake Optimizer (SO), termed VMD-SO-CNN-LSTM-MATT. VMD decomposes complex subsidence signals into stable intrinsic components, improving input data quality. The SO algorithm is introduced to globally optimize model parameters, preventing local optima and enhancing prediction accuracy. This model utilizes time–series subsidence data extracted via the SBAS-InSAR technique as input. Initially, the original sequence is decomposed into multiple intrinsic mode functions (IMFs) using VMD. Subsequently, a CNN-LSTM network incorporating a Multi-Head Attention mechanism (MATT) is employed to model and predict each component. Concurrently, the SO algorithm performs global optimization of the model hyperparameters. Experimental results demonstrate that the proposed model significantly outperforms comparative models (traditional Long Short-Term Memory (LSTM) neural network, VMD-CNN-LSTM-MATT, and Sparrow Search Algorithm (SSA)-optimized CNN-LSTM) across key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Specifically, the reductions achieved are minimum improvements of 29.85% for MAE, 8.42% for RMSE, and 33.69% for MAPE. This model effectively enhances the prediction accuracy of land subsidence in cultivated hilly and mountainous areas, validating its high reliability and practicality for subsidence monitoring and prediction tasks. Full article

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide. Full article

In deep excavation dewatering engineering, fully enclosed cutoff walls are widely implemented to improve the efficiency of dewatering in the pit and prevent adverse environmental impacts such as land subsidence and damage to adjacent infrastructure. However, the presence of such impermeable barriers fundamentally alters flow dynamics, rendering conventional aquifer test interpretation methods inadequate. This study presents a novel closed-form analytical solution for transient drawdown in a leaky confined aquifer bounded by a rectangular, fully enclosed cutoff wall under constant-rate pumping. The solution is rigorously derived by applying the mirror image method within a superposition framework, explicitly accounting for the barrier effect of the curtain. A type-curve matching methodology is developed to inversely estimate key aquifer parameters—transmissivity, storativity, and vertical leakage coefficient—while incorporating the geometric and boundary effects of the curtain. The approach is validated against field data from a pumping test conducted at a deep excavation site in Wuhan, China. Excellent agreement is observed between predicted and measured drawdowns across multiple observation points, confirming the model’s fidelity. The proposed solution and parameter estimation technique provide a physically consistent, analytically tractable, and computationally efficient framework for interpreting pumping tests in constrained aquifer systems, thereby improving predictive reliability in dewatering design and supporting sustainable groundwater management in urban underground construction. Full article

Because of its specific nature, mining activity causes numerous negative impacts on the environment, both during the exploitation phase and after it has ended. An important source of income in the Jiu Valley is represented by the Lupeni Mining Exploitation. Like any mining activity, coal exploitation causes various negative effects on the environment. The subsidence phenomenon represents a significant issue associated with coal mining in the Jiu Valley. Underground extraction of mineral deposits induces displacement of the overburden strata. Such displacements result in ground subsidence and modifications of the surface topography. The larger the voids created following the exploitation of useful mineral deposits, the more they affect the surface of the land above the exploitation through sinking, displacement, deformation, and even cracks. Secondary deformations refer to post-mining surface movements induced by delayed rock mass adjustment, manifesting as ground collapse, localized subsoil failure, or uplift driven by groundwater rebound after drainage cessation. In this paper, we aim to study the subsidence phenomenon produced by coal mining at the Lupeni Mining Exploitation using the COMSOL simulation software and applying the Barcelona Basic Model (BBM) and Modified Cam-Clay (MCC) models. Following the simulation, the behavior of the rocks could be observed in order to improve prediction accuracy to support sustainable land management in post-mining areas. Full article

To address the building settlement issues induced by an urban geological hazard in a northern city, this study utilizes settlement monitoring data from 16 high-rise buildings. The non-uniform temporal data were processed using the Akima interpolation method to construct a settlement prediction model based on a backpropagation (BP) neural network. The model’s predictive performance was validated against traditional approaches, including the hyperbolic and exponential curve methods, and was further employed to estimate the stabilization time of building settlements. Additionally, spatiotemporal characteristics of settlement behavior under the influence of geological hazards were investigated through a comparative analysis of deformation data across the building group. The results demonstrate that the BP neural network model achieves a 58.3% improvement in predictive accuracy compared to traditional empirical methods, effectively capturing the settlement evolution of buildings. The model also provides reliable predictions for the time required for buildings to reach a stable state. The temporal evolution of building settlement exhibits a distinct three-stage pattern: (1) an initial abrupt phase dominated by rapid water and soil loss; (2) a rapid settlement phase primarily driven by the consolidation of sandy and clayey soils; and (3) a slow consolidation phase governed by the prolonged consolidation of cohesive soils. Spatially, building deformations show significant regional heterogeneity, and the existence of potential finger-like preferential pathways for water and soil loss appears to exert a substantial influence on differential settlements. Full article

Mining-induced surface subsidence is a typical geological hazard. Loose layer drainage disturbed by coal mining can exacerbate surface subsidence in terms of both the extent and amount, thereby increasing the risk of building deformation and environmental degradation in mining areas. However, currently the prediction results of surface subsidence considering these two factors are not precise enough, which contradicts the principles of green coal mining. Firstly, this paper introduces the probability integral method, which predicts mining-induced surface subsidence. Subsequently, based on the soil–water coupled theory and the derived characteristic curve of groundwater level decline, a surface subsidence prediction model that considers loose layer drainage is constructed using triple integral transformation. Finally, a more precise surface subsidence prediction model considering both factors is proposed based on the principle of superposition. The model is applied to the mining of working panel 1309 in Shanxi province, China, an area rich in coal yet scarce in water resources. When compared with the measured subsidence data, the proposed model achieves a root mean square error (RMSE) of 27 mm, while the RMSEs of existing models are 78 mm and 123 mm, respectively. The prediction accuracy has been significantly improved. In addition, the proposed model is further validated through fluid–solid coupling numerical calculations in FLAC3D. The subsidence results considering the single effect of each factor also demonstrated good validation accuracy. Overall, the proposed model can accurately describe the surface subsidence considering both factors. This research can provide a theoretical guide for assessing the environmental impact and building damage, while contributing to the sustainable development of land use and groundwater resource in mining areas. Full article

In mining areas with high groundwater levels, intensive coal mining has led to the accumulation of substantial surface water and significant alterations in regional landscape patterns. Reconstructing the ecological security pattern (ESP) has emerged as a critical focus for ecological restoration in coal mining subsidence areas with high groundwater levels. This study employed the patch-generating land use simulation (PLUS) model to predict the landscape evolution trend of the study area in 2032 under three scenarios, combining environmental characteristics and disturbance features of coal mining subsidence areas with high groundwater levels. In order to determine the differences in ecological network changes within the study area under various development scenarios, morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were employed to identify ecological source areas and establish ecological corridors using circuit theory. Based on the simulation results of the optimal development scenario, potential ecological pinch points and ecological barrier points were further identified. The findings indicate that: (1) land use changes predominantly occur in urban fringe areas and coal mining subsidence areas. In the land reclamation (LR) scenario, the reduction in cultivated land area is minimal, whereas in the economic development (ED) scenario, construction land exhibits a marked increasing trend. Under the natural development (ND) scenario, forest land and water expand most significantly, thereby maximizing ecological space. (2) Under the ND scenario, the number and distribution of ecological source areas and ecological corridors reach their peak, leading to an enhanced ecological network structure that positively contributes to corridor improvement. (3) By comparing the ESP in the ND scenario in 2032 with that in 2022, the number and area of ecological barrier points increase substantially while the number and area of ecological pinch points decrease. These areas should be prioritized for ecological protection and restoration. Based on the scenario simulation results, this study proposes a planning objective for a “one axis, four belts, and four zones” ESP, along with corresponding strategies for ecological protection and restoration. This research provides a crucial foundation for decision-making in enhancing territorial space planning in coal mining subsidence areas with high groundwater levels. Full article

The Eastern Huang–Huai region of China is a representative mining area with a high groundwater level. High-intensity underground mining activities have not only induced land cover and land use changes (LUCC) but also significantly changed the watershed hydrological behavior. This study integrated the land use prediction model PLUS and the hydrological simulation model MIKE 21. Taking the Bahe River Watershed in Huaibei City, China, as an example, it simulated the hydrological response trends of the watershed in 2037 under different land use scenarios. The results demonstrate the following: (1) The land use predictions for each scenario exhibit significant variation. In the maximum subsidence scenario, the expansion of water areas is most pronounced. In the planning scenario, the increase in construction land is notable. Across all scenarios, the area of cultivated land decreases. (2) In the maximum subsidence scenario, the area of high-intensity waterlogging is the greatest, accounting for 31.35% of the total area of the watershed; in the planning scenario, the proportion of high-intensity waterlogged is the least, at 19.10%. (3) In the maximum subsidence scenario, owing to the water storage effect of the subsidence depression, the flood peak is conspicuously delayed and attains the maximum value of 192.3 m³/s. In the planning scenario, the land reclamation rate and ecological restoration rate of subsidence area are the highest, while the regional water storage capacity is the lowest. As a result, the total cumulative runoff is the greatest, and the peak flood value is reduced. The influence of different degrees of subsidence on the watershed hydrological behavior varies, and the coal mining subsidence area has the potential to regulate and store runoff and perform hydrological regulation. The results reveal the mechanism through which different land use scenarios influence hydrological processes, which provides a scientific basis for the territorial space planning and sustainable development of coal mining subsidence areas. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts. Full article