Search Results (22)

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction. Full article

Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts. Full article

This paper presents an assessment of land stability using fallout environmental radioisotopes ⁷Be and ¹³⁷Cs. The measurement of both isotopes was carried out in samples of soil collected from twenty-five sites covering the studied region. At each site, the samples were taken from five consecutive vertical depth levels to show the vertical displacement or compactness of the soil column. The collected samples were carefully transferred for radioactivity measurement at Alexandria University’s Institute of Graduate Studies and Research. A high-resolution gamma-ray spectrometer utilizing high-purity germanium was employed for the measurements. Surface distribution of the radionuclides levels was used to show the studied lands’ stability over the short- and long-term based on the used radionuclides’ nuclear half-life. For short-term (months) stability, ⁷Be (half-life: 35.5 days) levels showed that about 73% of the area is very low in stability, while the areas that recorded low, moderate, and high stability are at 18%, 4%, and 5%, respectively. For long-term (years) stability, ¹³⁷Cs (half-life: 30 years) levels showed that about 80% of the areas are very low in stability, while the remaining areas, predicted as 12.8%, 5.6%, and 1.6%, are low, moderate, and high stability, respectively. It is clear that the eastern side of Alexandria is suffering from soil erosion and subsidence; on the other hand, the western side is more stable. Consequently, due to the origin of the soil, the nature of soil geological formations, and the environmental prevailing conditions, Alexandria is found to be more vulnerable to the consequences of sea-level rise and climate change. Therefore, adequate strategic management, including mitigation measures and adaptation, should be planned and implemented. Full article

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations. Full article

Land subsidence is a prevalent geological issue that poses significant challenges to construction projects. Consequently, the accurate prediction of land subsidence has emerged as a focal point of research among scholars and experts. Traditional mathematical models exhibited certain limitations in forecasting the extent of land subsidence. To address this issue, the sparrow search algorithm (SSA) was introduced to optimize the efficacy of the long short-term memory (LSTM) neural network in land subsidence prediction. This prediction model has been successfully applied to the Huanglong Commercial City project in the Guanghua unit of Wenzhou city, Zhejiang province, China, and has been compared with the predictions of other models. Using monitoring location 1 as a reference, the MAE, MSE, and RMSE of the test samples for the LSTM neural network optimized using the SSA are 0.0184, 0.0004, and 0.0207, respectively, demonstrating a commendable predictive performance. This new model provides a fresh strategy for the land subsidence prediction of the project and offers new insights for further research on combined models. Full article

The land subsidence occurring over a goaf area after coal mining is a protracted process. The accurate prediction of long-term land subsidence over goaf areas relies heavily on the availability of long-term land subsidence monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on interferometric synthetic aperture radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No. 2 mine in Shanxi Province, China. Initially, coal faces with the same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation on each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No. 2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term land subsidence monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf. Full article

The subway alleviates the traffic pressure in the city but also brings the potential risk of land subsidence. The land subsidence caused by the subway is a global problem that seriously affects the safety of subway operations and surrounding buildings. Therefore, it is very important to carry out long-term deformation monitoring on the subway system. StaMPS-PS is a time-series Interferometric Synthetic Aperture Radar (InSAR) technique that serves as an effective means for monitoring urban ground subsidence. However, the accuracy of external (Digital Elevation Models) DEM will affect the accuracy of StaMPS-PS monitoring, and previous studies have mostly used SRTM-1 arc DEM (30 m) as the external DEM. In this study, to obtain a more precise measurement of surface deformation caused by the excavation of the Hohhot subway, a total of 85 scenes of Sentinel-1A data from July 2015 to October 2021, as well as two different resolution digital elevation models (DEMs) (ALOS PALSAR DEM and SRTM-1 arc DEM), were used to calculate and analyze the subsidence along the subway line in Hohhot city. The StaMPS-PS monitoring results showed the ALOS PALSAR DEM, as an external DEM, had higher accuracy, and there was regional subsidence in both the construction processes of Line 1 and Line 2 of the Hohhot subway, with a maximum subsidence rate of −21.1 mm/year. The dynamic changes in subway subsidence were fitted using the Peck formula and the long short-term memory (LSTM) model. The Peck formula results showed the width and maximum subsidence of the settlement troughs gradually expanded during the construction of the subway. The predicted values of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the LSTM model were less than 4 mm and 10%, respectively, consistent with the measured results. Furthermore, we discussed the factors that affect settlement along the subway line and the impact of two external DEMs on StaMPS-PS. The study results provide a scientific method for DEM selection and subsidence analysis calculations in the StaMPS-PS monitoring of urban subway subsidence. Full article

Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information. Full article

In recent years, the groundwater level (GWL) and its dynamic changes in the Hebei Plain have gained increasing interest. The GWL serves as a crucial indicator of the health of groundwater resources, and accurately predicting the GWL is vital to prevent its overexploitation and the loss of water quality and land subsidence. Here, we utilized data-driven models, such as the support vector machine, long-short term memory, multi-layer perceptron, and gated recurrent unit models, to predict GWL. Additionally, data from six GWL monitoring stations from 2018 to 2020, covering dynamical fluctuations, increases, and decreases in GWL, were used. Further, the first 70% and remaining 30% of the time-series data were used to train and test the model, respectively. Each model was quantitatively evaluated using the root mean square error (RMSE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE), and they were qualitatively evaluated using time-series line plots, scatter plots, and Taylor diagrams. A comparison of the models revealed that the RMSE, R², and NSE of the GRU model in the training and testing periods were better than those of the other models at most groundwater monitoring stations. In conclusion, the GRU model performed best and could support dynamic predictions of GWL in the Hebei Plain. Full article

In the field of geotechnical engineering, the problems of liquefaction and land subsidence are of major concern. In order to mitigate or prevent damage from liquefaction, the chemical injection method is actively used as one of the countermeasures for ground improvement. However, a complete understanding of the long-term sustainability of improved grounds is still unavailable due to a lack of knowledge of the influencing parameters. Thus, the chances of chemical injection accidents cannot be ruled out. In this study, the compressive strength of improved grounds by the granulated blast furnace slag (GBFS), one of the grouting materials used in the chemical injection method, was evaluated and used for a time-series prediction of long-term sustainability. The objective of this study was to evaluate the accuracy and validity of the prediction method by comparing the prediction results with the test results. The study was conducted for three different models, namely, the autoregressive integrated moving average (ARIMA) model, the state-space representation (SSR) model, and the machine learning predictive (MLP) model. The MLP model produced the most reliable results for the prediction of long-term data when the input information was sufficient. However, when the input data were scarce, the SSR model produced more reliable results overall. Meanwhile, the ARIMA model generated the highest degree of errors, although it produced the best results compared to the other models depending on the criteria. It is advised that studies should be continued in order to identify the parameters that can affect the long-term sustainability of improved grounds and to simulate various other models to determine the best model to be used in all situations. However, this study can be used as a reference for the selection of the best prediction model for similar patterned input data, in which remarkable changes are observed only at the beginning and become negligible at the end. Full article

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity. Full article

With the rapid development of social economy in China, the contradiction between the wide distribution of abandoned goaf and the shortage of land for engineering construction is becoming increasingly prominent. The effective utilization of coal mining subsidence areas has become an effective measure to alleviate the poverty of construction land in mining areas and promote the green transformation of mining cities. The key to the scientific utilization of abandoned goaf is the prevention and control of surface residual deformation, which depends on the formation mechanism of surface residual deformation. Based on the regularity of mining-induced surface movement and deformation under different mining sizes, it is concluded that the full mining degree of working face is the primary condition for entering the surface recession period. The trapezoidal and periodic forward movement characteristics of mining-induced overburden destruction are analyzed. The regularity of upward transmission of mining-induced fissures with overburden destruction is clarified. The influencing factors of surface residual deformation are equivalent to the influencing factors of overburden structure and caved zone. The deformation characteristics of broken rock in the caved zone under different conditions (particle size, gradation, and water content) are analyzed. It is concluded that the surface residual subsidence near the boundary of the goaf is more significant than that in the middle of the goaf. It is revealed that the overburden structure at the boundary of the goaf and the re-compaction of the caved zone is the mechanism of surface residual deformation. The characteristics of surface residual deformation in abandoned goaf have been verified by field measurement, and it is pointed out that the surface residual deformation in abandoned goaf has long-term characteristics, which provides a theoretical basis for accurate prediction of surface residual deformation and rational utilization of abandoned goaf. Full article

Daily groundwater level is an indicator of groundwater resources. Accurate and reliable groundwater level (GWL) prediction is crucial for groundwater resources management and land subsidence risk assessment. In this study, a representative deep learning model, long short-term memory (LSTM), is adopted to predict groundwater level with the selected predictors by partial mutual information (PMI), and bootstrap is employed to generate different samples combination for training many LSTM models, and the predicted values by many LSTM models are used for the uncertainty assessment of groundwater level prediction. Two wells of different climate zones in the USA were used as a case study. Different significant predictors of GWL for two wells were identified by PMI from candidate predictors incorporating teleconnection patterns information. The results show that GWL is significantly affected by antecedent GWL, AO, Niño 3.4, Niño 1 + 2, and precipitation in humid areas, and by antecedent GWL, AO, Niño 3.4, Niño 3, Niño 1 + 2, and PNA in arid areas. Predictor selection can assist in improving the prediction performance of the LSTM model. The relationship between GWL and significant predictors were modeled by the LSTM model, and it achieved higher accuracy in humid areas, while the performance in arid areas was poorer due to limited precipitation information. The performance of LSTM was improved by increasing correlation coefficient (R²) values by 10% and 25% for 2 wells compared to generalized regression neural network (GRNN). Three uncertainty evaluation metrics indicate that LSTM reduced the uncertainty compared to GRNN model. LSTM coupling with PMI and bootstrap can be a promising approach for accurate and reliable groundwater level prediction for different climate zones. Full article

The exploitation of underground coal resources has stepped up local economic and social development significantly. However, it was inevitable that time-dependent surface settlement would occur above the mined-out voids. Subsidence associated with local geo-mining can last from several months to scores of years and can seriously impact infrastructure, city planning, and underground space utilization. This paper addresses the problems in predicting progressive residual surface subsidence. The subsidence process was divided into three phases: a duration period, a residual subsidence period, and a long-term subsidence period. Then, a novel mathematical model calculating surface progressive residual subsidence was proposed based on the logistic time function. After the duration period, the residual subsidence period was extrapolated according to the threshold of the surface sinking rate. The validation for the proposed model was estimated in light of observed in situ data. The results demonstrate that the logistic time function is an ideal time function reflecting surface subsidence features from downward movement, subsidence rate, and sinking acceleration. The surface residual subsidence coefficient, which plays a crucial role in calculating surface settling, varies directly with model parameters and inversely with time. The influence of the amount of in situ data on predicted values is pronounced. Observation time for surface subsidence must extend beyond the active period. Thus back-calculated parameters with in situ measurement data can be reliable. Conversely, the deviation between predictive values and field-based ones is significant. The conclusions in this study can guide the project design of surface subsidence measurement resulting from longwall coal operation. The study affords insights valuable to land reutilization, city planning, and stabilization estimation of foundation above an abandoned workface. Full article

Finding a suitable location is a key factor for long-term investment in wind turbine energy. It includes understanding the area of interest, such as the subsidence of the land. Land subsidence is a gradual decrease in the surface of the Earth due to natural and/or induced causes. It can cause damage, such as settlement problems in the ground near infrastructure including buildings and wind turbines, thus not being a suitable place for long-term investment. Here, we show a case study of land subsidence prediction and assessment of the Atsimo Andrefana region, the great south-western part of Madagascar, using theoretical simulation and satellite images from the Sentinel-1 mission using D-InSAR method. The predicted land subsidence related to the depletion of groundwater reservoirs in the Atsimo Andrefana region is around 12 mm. We found ~5 mm of subsidence related to the growing city of Toliary and with an average subsidence of 124 mm and the highest record of 167 mm in the most southern part of the region for a period of 6 months. The spatial distribution of land subsidence allows us to choose the ideal location for wind turbine settlement, where land subsidence is not that severe, i.e., the areas with subsidence relatively low of equal or less than 10 mm within 6 months of observation, based on the processed data. Such results are essential for future environmentally friendly investments in the affected region, as the demand for green energy will always grow. Full article