Subsidence Research Articles

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan’s Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.

Preventing Subsidence Reoccurrence in Tianjin: New Preconsolidation Head and Safe Pumping Buffer.

Tianjin, a coastal metropolis in north China, has grappled with land subsidence for nearly a century. Yet, emerging evidence suggests a notable decrease in subsidence rates across Tianjin since 2019. This trend is primarily attributed to the importation of surface water from the Yangtze River system via the South-to-North Water Diversion Project, initiated in December 2014. Utilizing Sentinel-1A Interferometric Synthetic Aperture Radar (InSAR) data (2014-2023), this study reveals that one-third of the Tianjin plain has either halted subsidence or experienced land rebound. As a result, the deep aquifer system (~-200 to -450 m) beneath one third of the Tianjin plain has completed a consolidation cycle, leading to the establishment of new, locally specific preconsolidation heads. The identification of the newly established preconsolidation head seeks to answer a crucial question: How can we prevent the reoccurrence of subsidence in areas where it has already ceased? In essence, subsidence will stop when the local hydraulic head elevates to the new preconsolidation head (NPCH), and permanent subsidence will not be reinitiated as long as hydraulic head remains above the NPCH. The difference of the depth between current hydraulic head and the NPCH defines the safe pumping buffer (SPB). This study outlines detailed methods for identifying the NPCHs in the deep aquifer system from long-term InSAR and groundwater-level datasets. Determining NPCHs and ascertaining SPBs are crucial for estimating how much groundwater can be safely extracted without inducing permanent subsidence, and for developing sustainable strategies for long-term groundwater management and conservation.

Evaluation of Slope Stability on Dam Using Ground-based Interferometric Radar

Dams are man-made structures built to manage water resources efficiently and prepare for natural disasters such as droughts and floods. It requires careful and continuous inspection to prevent its failure. Research reported to assess dam stability using terrestrial surveys such as ground penetration radar, electrical resistivity tomography, and remote sensing methods such as space-borne synthetic aperture radar (SAR). Differential interferometric SAR (DInSAR) calculates the phase difference between two consecutive images acquired at separate times and has been widely utilized to detect surface displacement from volcanoes, earthquakes, and ground subsidence. However, space-borne InSAR applications have limitations in acquiring flexible data for specific dates or regions due to the revisit cycle of the orbital configuration and the fixed acquisition geometry. In this feasibility study, the slope stability of the dam was evaluated using the Gamma Portable Radar Interferometer-II (GPRI-II) which has the advantage of overcoming the limitation of satellite observations. The GPRI-II is a ground-based real aperture radar that operates in the Ku-band wavelength (~1.7 cm), providing convenient portability and installation for high spatial and temporal resolution. A total of 20 GPRI-II datasets were acquired for 22 minutes on June 7, 2023, at a dam in Jeollanam-do for the DInSAR application. The displacement calculation revealed an average displacement of approximately -0.36 mm at a randomly selected point, which is negligible. The average displacement of -0.17 mm was observed for the entire dam. Our results suggest that ground-based radar interferometry could assess the dam slope stability.

Ground Subsidence, Driving Factors, and Risk Assessment of the Photovoltaic Power Generation and Greenhouse Planting (PPG&GP) Projects in Coal-Mining Areas of Xintai City Observed from a Multi-Temporal InSAR Perspective

In recent years, photovoltaic power generation and greenhouse planting (PPG&GP) have become effective approaches for reconstructing and restoring the ecological environment of old coal-mining industry bases, such as Xintai City. However, the ecological impacts or improvements of the PPG&GP projects and their daily operations on the local environment are still unclear. To solve these problems, this study retrieved the ground deformation velocities and time series of the study region by performing the Small-Baseline Subset (SBAS)-Interferometric Synthetic Aperture Radar (InSAR) technique on the Advanced Land Observing Satellite (ALOS) PALSAR and Sentinel-1 SAR datasets. With these deformation results, the spatial analysis indicated that the area of the subsidence region within the PPG&GP projects reached 10.70 km2, with a magnitude of approximately −21.61 ± 12.10 mm/yr. Also, even though the ground deformations and their temporal changes were both visible in the construction and operation stages of the PPG&GP projects, the temporal analysis demonstrated that most observation points finally entered into the stationary phases in the late stage of the observation period. This phenomenon validated the effectiveness of the PPG&GP projects in enhancing the ground surface stability in coal-mining areas. Additionally, the precipitation, geological structure, increased coal-mining depths, and emergent agricultural modes were assumed to be the major impact factors controlling the ground deformation within the local PPG&GP projects. Finally, a novel risk assessment method with a designed index of IRA was utilized to classify the ground subsidence risks of the PPG&GP projects into three levels: Low (69.7%), Medium (16.9%), and High (9.4%). This study sheds a bright light on the ecological monitoring and risk management of the burgeoning industrial and agricultural infrastructures, such as the PPG&GP projects, constructed upon the traditional coal-mining areas in China from a multi-temporal InSAR perspective.

Sea level rise and extreme events along the Mediterranean coasts: the case of Venice and the awareness of local population, stakeholders and policy makers

Sea level rise (SLR) is among the major climate change effects threating the coasts of the Mediterranean basin, which are increasingly exposed to coastal flooding, especially along the low lying coastal plains, river deltas, lagoons and reclamation areas. Coastal erosion, beach retreat and marine flooding are already causing unprecedented environmental and socio-economic impacts on coastal populations. According to the Intergovernmental Panel on Climate Change (IPCC) these effects are expected to worsen by 2100 and beyond with a projected global SLR up to about 1 m above the current level. This study provides an overview of the Mediterranean basin, focusing on the vulnerable city of Venice, which is particularly exposed to marine flooding due to SLR and land subsidence. We show the current and future sea level trend as well as a flooding scenarios in the absence of the Experimental Electromechanical Module (MoSE), which is protecting the city of Venice since 2020. To understand the awareness of citizens in Venice to address SLR, we have engaged a group of stakeholders through a structured participatory process to develop solution-oriented, case-specific and site-specific Policy Tools. Our results show that the Policy Tools contain relevant, effective and implementable actions stemming from stakeholder interaction and consensus building, identifying relevant issues that should be considered for SLR adaptation policies. A more extensive participation in public processes is required to materialize the Policy Tools into concrete actions to help vulnerable areas adapt to the expected SLR by the end of this century.

Ground deformation monitoring via PS-InSAR time series: An industrial zone in Sacco River Valley, central Italy

Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment.

Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer interferometry with deep learning algorithm optimization

Mine operational safety is an important aspect of maintaining the operational continuity of a mining area. In this study, we used the InSAR time series to analyze land surface changes using the ICOPS (improved combined scatterers with optimized point scatters) method. This ICOPS method combines persistent scatterers (PS) with distributed scatterers (DS) to increase surface deformation analysis’s spatial coverage and quality. One of the improvements of this study is the use of machine learning in postprocessing, based on convolutional neural networks, to increase the reliability of results. This study used data from the Sentinel-1 SAR C-band satellite during the 2016–2022 observation period at the Musan mine, North Korea. In the InSAR surface deformation time analysis, the maximum average rate of land subsidence was approximately > 15.00 cm per year, with total surface deformation of 170 cm and 70 cm for the eastern dumping area and the western dumping area, respectively. Analyzing the mechanism of land surface changes also involved evaluating the geological conditions in the Musan mining area. Our research findings show that combining machine learning and statistical methods has great potential to enhance the understanding of mine surface deformation.

Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands

For restoring soil health and mitigating climate change, information of soil organic matter is needed across space, depth and time. Here we developed a statistical modelling platform in three-dimensional space and time as a new paradigm for soil organic matter monitoring. Based on 869 094 soil organic matter observations from 339,231 point locations and the novel use of environmental covariates variable in three-dimensional space and time, we predicted soil organic matter and its uncertainty annually at 25 m resolution between 0–2 m depth from 1953–2022 in the Netherlands. We predicted soil organic matter decreases of more than 25% in peatlands and 0.1–0.3% in cropland mineral soils, but increases between 10–25% on reclaimed land due to land subsidence. Our analysis quantifies the substantial variations of soil organic matter in space, depth, and time, highlighting the inadequacy of evaluating soil organic matter dynamics at point scale or static mapping at a single depth for policymaking.

Efforts to Increase the Capacity of Sukorejo Village, Semarang City Community in Facing Landslide Disasters Through the Preparation of Evacuation Standard Operating Procedures (SOPs)

This study identified the problem of landslide disasters that often occur in Sukorejo Village, Gunungpati District, Semarang City. Sukorejo village belongs to the high ground movement zone which has a high degree of vulnerability to soil movement. The purpose of the study was to determine efforts to increase the capacity of the community in facing landslide disasters through the preparation of evacuation standard operating procedures (SOPs). The data collection of this study used interview, observation, documentation, and geographic information systems (GIS) methods. Data analysis is carried out with the approach of spatial analysis and qualitative descriptive analysis. The results showed that the preparation of evacuation SOPs is very important to increase the capacity of the community during landslide emergency response. The recommendations given are the need to add an Early Warning System (EWS) tool, install evacuation route signs, and gathering points in landslide-prone areas. In addition, the community also needs to increase awareness and preparedness in the face of possible landslides. This research contributes to improving understanding of landslide disaster mitigation efforts and can be followed up by assessing community capacity and evaluating future evacuation SOPs.

Geohazard assessment of Mexico City’s Metro system derived from SAR interferometry observations

Land subsidence rates in Mexico City reach 500 mm/year, causing progressive damage to the city’s core infrastructure, including the Metro system. A deadly overpass collapse in 2021, along a Metro line that had operated for less than 10 years, brought subsidence-related structural damage to the attention of the system’s authorities and led to major repairs to two of the twelve Metro lines. Still, the need for quantifying the magnitude and extent of subsidence affecting the Metro system’s widespread infrastructure prevails. Using a wealth of satellite radar interferometry observations, levelling surveys, subsurface profiles, linear gradient and differential displacement analyses, and structural-engineering parameters, we assess the vulnerability of the Metro system’s street-level and elevated segments to land subsidence. Our results reveal that high subsidence velocity gradients occur over sharp transitional zones between stable and fast-subsiding areas, reaching values of 1 ×10-3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes \\,10 ^{-3}$$\\end{document} year-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}, resulting in slope changes up to 3.5% over a 20-year period and differential displacements between columns. Our findings suggest locations where the consequences of subsidence have compromised the train’s braking safety design, increased railway flooding hazard, produced railway bending, and reduced the conceived 50-year service life of the Metro’s elevated overpasses.

Land subsidence along the Beijing-Tianjin high-speed railway before and after the South-to-North water diversion project with multi-source monitoring datasets

The development of land subsidence has seriously affected the safe operation of Beijing-Tianjin high-speed railway. The South-to-North Water Diversion Project Central Route (SNWDP-CR) was officially put into operation in December 2014. It has changed the water supply pattern in Beijing and provided conditions for reducing groundwater exploitation and controlling land subsidence. In this paper, the time-series interferometric data, in situ monitoring data of recent 20 years and the basic geological datasets are combined to compare and analyze the changes of groundwater level, land subsidence and the main subsidence layers along the Beijing-Tianjin high-speed railway before and after the SNWDP-CR. The effects of the environment of Quaternary sedimentary, groundwater exploitation and soil deformation of different lithology on land subsidence along the high-speed railway under the background of new water conditions are revealed. The main conclusions are as follows: 1) The serious land subsidence area along the Beijing-Tianjin high-speed railway always concentrated in the section of DK11-DK23. After the operation of SNWDP-CR, the land subsidence along the railway generally showed a slowing trend. The maximum subsidence rate was reduced from 80 mm/yr to 49 mm/yr. The length of subsidence rate that more than 50 mm/yr of the section was reduced from 8.0 km to 0 km. 2) The groundwater level of different aquifer groups along the Beijing-Tianjin high-speed railway rose and declined before and after the SNWDP-CR. in eastern part of the plain, the groundwater level of each aquifer group has changed from a continuous decline (range 0.13–1.82 m) to a gradual rise (range 0.45–1.87 m) since 2017. However, in the southeast of the plain, the groundwater level still showed a continuous decline trend, with an average annual decline of 1.2–1.8 m. 3) From 2006 to 2019, the subsidence of the first, second and third compression layer group along the railway accounted for 2.71%, 28.29% and 69%, respectively. The third compression layer group (monitoring layer 94–182 m) had the largest subsidence proportion and was the main subsidence layer. 4) The land subsidence along the Beijing-Tianjin high-speed railway is controlled by the basement structure. The difference of groundwater exploitation intensity led to differences in the spatial distribution of land subsidence along the railway. The subsidence of the soil layer below the bearing layer (about 50 m) of the high-speed railway pile foundation exhibited the characteristics of viscoplastic or viscoelastic plasticity deformation. This section of strata is a key layer that needs to be considered for land subsidence control along the Beijing-Tianjin high-speed railway in the future.

Determining land subsidence potential using the evidential belief function model: A case study for the Bardaskan Aquifer, Iran

AbstractIn this study, we employed the evidential belief function model (EBF) to evaluate the potential for land subsidence in the primary aquifer of Bardaskan. Through field visits, we recorded GPS coordinates for 174 land subsidence points. Factors considered in assessing land subsidence potential included well density, groundwater extraction rate, geological characteristics, proximity to faults, vegetation cover, distance from the river, slope, and land use. To develop and validate the model, 70% of the recorded points were randomly selected for training and implementation, while the remaining 30% were reserved for model validation. The number and percentage of land subsidence points in the different classes of the corresponding layers were determined by integrating the training points with influential variables maps such as distance from the river, distance from the fault, land use, and extraction volume. The EBF model rate was calculated for different layer classes. For modeling, all rates of the EBF model in each cell were summated, and the ‎potential of land subsidence was calculated.‎ Finally, the map of land subsidence potential based on the EBF model was determined with GIS. The results showed that most of the subsidence points were located in alluvial sediment of the Holocene period, in areas with high groundwater harvesting, a distance of at least 3000 m from a river, a distance of at least 6000 m from a fault, low‐density rangelands, slopes of at least 0%–2%, and farmlands and gardens. A receiver operating characteristic curve analysis of the EBF model showed that it could accurately predict land subsidence in 87.5% of cases using 30% of the validation data. This suggests that the model can be used for practical applications.

Parallel finite layer method for land subsidence and its homotopy parameter inversion

Land subsidence is caused by the interaction between groundwater flow and soil deformation. At present, in the field of land subsidence calculation, there is an increasing tendency to use Biot's consolidation theory to precisely simulate the coupling processes of flow and deformation. However, there are some practical problems with large-scale numerical computation, such as large computational loads, low efficiency, and difficulty in obtaining parameters. The Finite Layer Method employs orthogonality of analytical functions to achieve decoupling among various series terms. And the method can be enhanced through parallel computing to optimize the efficiency of solving complex and extensive problems due to its decoupling property. Based on this, combined with the nonlinear homotopy method, a new approach for the reversal of land subsidence is proposed. The validity of the parallel program is confirmed through comparison with established solutions, and the impact of various factors on the efficiency of parallel computing is analyzed. The results indicate that the parallel approach enhances computational efficiency, while the homotopy inversion technique exhibits broad convergence characteristics.

Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology

The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area.

Climate change influences on the streamflow and sediment supply to the Chao Phraya River basin, Thailand

This study investigates the effects of climate change on the sediment loads of the Ping and Wang River basins and their contribution to the sediment dynamics of the lower Chao Phraya River basin in Thailand. The various climate models under different Representative Concentration Pathways (RCPs) scenarios are employed to project sediment loads in future. The findings indicate a significant increase in river flow approximately 20% in the Ping River (PR) and 35% in the Wang River (WR) by the mid-21st century and continuing into the distant future. Consequently, this is expected to result in sediment loads up to 0.33 × 106 t/y in the PR and 0.28 × 106 t/y in the WR. This escalation is particularly notable under the RCP 8.5 scenario, which assumes higher greenhouse gas emissions. Additionally, the research provides insights into the potential positive implications for the Chao Phraya Delta's coastal management. Without further damming in the Ping and Wang River basins, the anticipated rise in sediment supply could aid in mitigating the adverse effects of land subsidence and sea-level rise, which have historically caused extensive shoreline retreat in the delta region, particularly around Bangkok Metropolis. The paper concludes that proactive adaptation strategies are required to manage the expected changes in the hydrological and sediment regimes to protect vulnerable coastal zones and ensure the sustainable management of the Chao Phraya River Basin in the face of climate change.

Disappearing cities on US coasts

The sea level along the US coastlines is projected to rise by 0.25–0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities1–3. However, these impacts may be exacerbated by coastal subsidence—the sinking of coastal land areas4—a factor that is often underrepresented in coastal-management policies and long-term urban planning2,5. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000–273,000 people and 31,000–171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.

Assessment of coastal and mangrove vulnerability in the Andaman Island, Indian Ocean

Coastal regions are highly susceptible to the effects of global warming, including rising atmospheric and sea surface temperatures, increased cyclone frequency, and sea level rise. Thus, it is imperative to examine coastal vulnerability to minimize the impact of multiple hazards and protect coastal resources, such as mangroves. Particularly in India studying the vulnerability of coastal zones of Andaman and Nicobar Islands which fall in seismic zone V is critical for conservation efforts. We conducted a vulnerability analysis of coastal zones impacted by the 2004 earthquake, causing varying degrees of ground upliftment and subsidence. We compared coastal vulnerability among sites that experienced uplift, no change, and subsidence (the southern portion). Our analysis utilized the Coastal and Mangrove Vulnerability Index (CVI and MVI) to measure and compare vulnerability in six zones distributed along uplift and subsidence gradient. High-resolution satellite imagery including WorldView-2, 3, and GeoEye-1 from year 2022 are utilized on this study. The CVI and MVI offers a good way to measure and compare vulnerabilities across sites and offer insights for better management. The CVI and MVI results indicate that approximately 34% of coastal grids and over 23% of mangrove grids across all zones are highly to extremely highly vulnerable. Subsided zones were found to be more vulnerable than uplifted zones. These findings suggest that large-scale natural disturbances such as tectonic displacement have the potential to impact coastal vegetation and mangrove cover can become even more vulnerable. In conclusion, our study emphasizes the importance of vulnerability analyses in coastal regions, especially in areas prone to seismic activity. Our findings have direct implications for conservation and restoration efforts and underscore the need for continued monitoring and mitigation efforts to safeguard coastal resources for long-term sustainability.

Lake bottom relief reconstruction and water volume estimation based on the subsidence rate of the post-mining area (Bytom, Southern Poland)

Mining activity leads to subsidence troughs and permanent changes in water relations, like the formation of anthropogenic reservoirs. In the Upper Silesian Coal Basin (S Poland), their number is so high that the area is called an anthropogenic lake district. Any form of water retention, in the face of climate change, is valuable. However, the problem is the high variability of these lakes, making it challenging to estimate water resources. An example of this type of anthropogenic lake is the Brandka Pond in Bytom. An original method was proposed, consisting of two stages: reconstruction of the lake bottom relief based on the initial state of the area relief in 1994, i.e. at the beginning of the reservoir formation, and the land subsidence rate calculated for this area. Archival cartographic materials and DEMs from LiDAR data were used and processed in the open-source geoinformation software. Orthophoto maps and satellite scenes were also collected to determine changes in the extent of the pond from 1993 to 2019. Bathymetric data obtained in 2019 during sonar measurements on the reservoir was used to verify the calculations. The pond began to form in the early 1990s, and by 2019, it had reached an area of 178,226 m2, a maximum depth of 5.8 m and a capacity of 421,173 m3. The reconstruction method is accurate and suitable for lakes over 2 m deep, and the calculated capacity differs from the bathymetric data by 0.2%.

Reliability assessment of land subsidence monitoring results using PSI technique in Ho Chi Minh City, Vietnam

ABSTRACT Groundwater exploitation, soft ground, and urban development are the main causes of land subsidence in Vietnam. This study focuses on identifying, zoning, monitoring, and evaluating land subsidence using PSI technique with IBI method applied to Ho Chi Minh City. The results show diverse land subsidence trends from 2014 to 2021 with different levels in varying locations 98,278 km2 had subsided by over 10 cm, 214,593 km2 had subsided by 5 to 10 cm, and 1.377,897 km2 had subsided by less than 5 cm. Notably, certain areas have higher subsidence funnel centres. To ensure accuracy when using PSI techniques, both natural and human-induced factors must be considered during data collection, which influences the actual subsidence rate. Though difficult, this technique provides reliable insights into this complex phenomenon, e.g. in the geodetic precise levelling method to detect millimetre-level deformations in urban areas. The average PS density in urban areas is between 0,5% and 2,5% of the original number of pixels. The positioning accuracy of a PS is within 1 m in all three directions if a large number of SAR scenes are used. Therefore, the results have been documented with 1 mm precision.

Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.