Land Subsidence Monitoring Research Articles

Understanding spatially nonstationary effects of natural and human-induced factors on land subsidence based on multi-temporal InSAR and multi-source geospatial data: a case study in the Guangdong-Hong Kong-Macao Greater Bay Area

ABSTRACTLand subsidence, a common geological phenomenon in deltaic regions, poses significant risks to infrastructures, environments, and human lives. Monitoring and understanding land subsidence are crucial for establishing resilient, adaptive, and sustainable environments. In this study, a robust multi-temporal interferometric synthetic aperture radar (MTInSAR) method was employed to monitor land subsidence in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) using 541 Sentinel-1 SAR images. Spatial auto- and cross-correlation analysis were applied to select the most reasonable explanatory variables from nine candidates in each city of the GBA. City-level geographically weighted regression (GWR) models were then constructed to explore the spatially nonstationary effects of natural and human-induced factors. The findings revealed subsidence velocities ranging from 0 to 83.7 mm/yr in the GBA from 2015 to 2021, with the dominant factors affecting subsidence varying among regions. Natural and human-related factors accounted for 51.13% and 48.87%, respectively. Further analysis of representative subsidence areas highlights the importance of continuous monitoring of ground deformation using MTInSAR, region-specific land subsidence mitigation strategies, and regular maintenance of urban infrastructure. These insights are valuable for policymakers and urban planners in comprehending the complex processes underlying land subsidence and informing decision-making processes for sustainable and resilient urban development.

Investigation of deformation coordination between optical fibre and borehole sand backfill

Land subsidence has threatened the safety of municipal infrastructures and even that of inhabitants. As one of the deformation monitoring methods, distributed optical fibre sensing (DOFS) technology has been developed for the investigation of land subsidence. The deformation coordination between the optical fibre and soil (DCf-s) under different conditions is critical for land subsidence monitoring with DOFS. In this paper, a medium-sized triaxial apparatus was modified for testing the DCf-s. Consolidated drained triaxial tests were conducted to investigate the effect of sand types on the DCf-s. By linearly fitting the deformation of the sensing cable with that of the triaxial specimen, the other factors that affect the DCf-s, such as the confining pressure, dry or wet state of the soil and cyclic variation of the loads, can be discussed. The experiments reveal that better DCf-s comes with a larger particle size, poor gradation of sand and larger confining pressure. The DCf-s of wet sand is better than that of dry sand. The DCf-s coefficient tends to be stable with an increase of loading cycles. The DCf-s obtained and its dependence on influencing factors can be used to modify the measured cable strain in practical land subsidence monitoring with DOFS.

An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data

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.

Excessive groundwater withdrawal and resultant land subsidence in the Küçük Menderes River Basin, Turkey as estimated from InSAR-SBAS and GNSS measurements

Küçük Menderes River Basin (KMRB) is located in the western part of Turkey in the city of İzmir. The basin hosts about half a million people and 90% of the population makes their living from farming and animal husbandry. Most of the water needs for agricultural irrigation, animal husbandry, and drinking are met from groundwater resources in the basin. Since the water pumped from the aquifers cannot be naturally recharged by surface waters and precipitation, the groundwater level is rapidly receding and a decrease of up to 150 meters has been observed in the historical groundwater level time series since the 1990s. For this reason, land subsidence occurred in the basin and as a result of this, crustal surface cracks have formed and infrastructure and superstructure facilities have been endangered. To prevent loss of life and property that may occur in the future, it has become necessary to estimate the magnitude and the expansion zone of the land subsidence by carrying out geodetic monitoring studies in the region.In this study, the analyzes performed by Interferometric Synthetic Aperture Radar-Small Baseline Subset (InSAR-SBAS) and Global Navigation Satellite System (GNSS) methods to monitor land subsidence in the KMRB are presented. For this purpose, monthly 74 SAR images from 2017 to the end of 2022 were evaluated according to the SBAS procedure in the GMTSAR academic analysis software. In order to, both validate the InSAR-SBAS results and convert the deformation rate in the Line of Sight (LOS) to the ellipsoid normal direction, 3 periods of GNSS campaign were performed between 2021 and 2023 in 5 different GNSS stations and evaluated with the GAMIT/GLOBK software. According to the analyses, the land subsidence rate of up to −165 mm/year has been estimated in the KMRB, especially in the district centers of Bayındır and Ödemiş. In addition, unlike many studies, different rates and propagation of subsidence were determined in different parts of the basin depending on the aquifer type and precipitation. Monthly time series of the deformations were created and the behavior of the deformations and its relationship with the groundwater withdrawal and the lithological structure in the region was scrutinized. Thus, the spatio-temporal behaviour of the land subsidence and the associated geological factors were revealed.

Dynamic Monitoring and Analysis of Mining Land Subsidence in Multiple Coal Seams in the Ehuobulake Coal Mine Based on FLAC3D and SBAS-InSAR Technology

Aiming at the land subsidence problem caused by multiple coal seam mining in the Ehuobulake Coal Mine, this paper, considering the geological conditions of the first and fifth layers of coal, adopts the method of combining FLAC3D numerical simulation and SBAS-InSAR technology to analyze the dynamic evolution law of land subsidence amount and range under multiple coal seam repeated mining conditions. The reliability of the technology is verified by the field GPS monitoring data. The results show that, under the mining condition of multiple coal seams in the Ehuobulake Coal Mine, the land subsidence presents obvious asymmetry, and the size and range of the land subsidence in the mining area further increase due to the mining of lower layer coal. FLAC3D simulation results show that the maximum land subsidence is −211.8 mm. The results of SBAS-InSAR monitoring show that the maximum land subsidence is −225 mm, and the land subsidence obtained by the two methods has a high degree of fitting. The method of combining FLAC3D and InSAR technology can accurately and reliably monitor and analyze the land subsidence under the repeated mining of multiple coal seams in the mining area. It can provide effective guidance for the stability analysis of mined-out areas and the prediction of the influence of repeated mining on ground deformation.

Geodetic/gravimetric evidence for mass loss in the subsidence area of Bandung, Indonesia

Land subsidence monitoring in Bandung, Indonesia, was initiated in the 2000 s. However, the monitoring has been limited to geometric observations only, which may restrict the further physical interpretation of the cause of the subsidence. In this study, we combine geometric and gravity observation methods to monitor surface subsidence in Bandung. 63 Synthetic Aperture Radar (SAR) images from Sentinel-1A covering the period of 2014–2020 were used to estimate the mean surface geometric changes. For the gravity observations, a hybrid gravity configuration that incorporates absolute (2008–2014) and relative (2011–2016) gravity observations were used to estimate the gravity changes. We estimated geometric changes of up to − 160 mm/yr, indicating rapid subsidence in the greater Bandung area. We obtained gravity changes ranging between − 56.7 and 40.1 μGal/yr. Upon subtracting the deformation-induced gravity field from the observed field, we produced a residual gravity field that was presumed to be dominated by the groundwater signal, which was then investigated further. We found that the gravity-derived groundwater signal was mainly negative, indicating subsurface mass loss. We further compared the signal with the modeled gravity effect from deep groundwater observations (1996–2008). The median difference between the observed and modeled groundwater gravity signal was estimated to be 2.8 ± 18.0 μGal/yr or equivalent to 0.08 ± 0.55 m/yr in terms of water height if we set the integration cap and groundwater depth to 1.4 km and 150 m, respectively. The discrepancy can be attributed to modeling (simple geohydrological assumption) and measurement (different observation periods and noise) factors. Nevertheless, both measurements indicate that the mass is decreasing due to groundwater depletion, demonstrating the potential of geometric-gravimetric observations to infer sub-surface mass loss.

Large-Scale Land Subsidence Monitoring and Prediction Based on SBAS-InSAR Technology with Time-Series Sentinel-1A Satellite Data

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.

Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City—A Case Study of Lanzhou, China

As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a long time series helps us grasp the patterns of change in various types of ground hazard. In this paper, we present the results of using Interferometric Synthetic Aperture Radar (InSAR) to monitor land subsidence in the main urban area of Lanzhou from 26 October 2014 to 12 December 2021. The main influential factors leading to subsidence were analyzed and combined via machine learning simulation to assess the land subsidence risk grade distribution of a building unit. The results show that the annual average deformation rate in Lanzhou ranged from −18.74 to 12.78 mm/yr. Linear subsidence dominated most subsidence areas in Lanzhou during the monitoring period. The subsidence areas were mainly distributed along the Yellow River, the railway, and villages and towns on the edges of urban areas. The main areas where subsidence occurred were the eastern part of Chengguan District, the railway line in Anning District, and the southern parts of Xigu District and Qilihe urban area, accounting for 38.8, 43.5, 32.5, and 51.8% of the area of their respective administrative districts, respectively. The random forest model analysis results show that the factors influencing surface subsidence in Lanzhou were, in order of importance, precipitation, the distribution of faults, the lithology of strata, high-rise buildings, and the distance to the river and railway. Lanzhou experienced excessive groundwater drainage in some areas from 2015 to 2017, with a 1 m drop in groundwater and 14.61 mm surface subsidence in the most critical areas. At the same time, extensive subsidence occurred in areas with highly compressible loess ground and most railway sections, reaching a maximum of −11.68 mm/yr. More than half of the super-tall building areas also showed settlement funnels. The area at a very high risk of future subsidence in Lanzhou covers 22.02 km2, while the high-subsidence-risk area covers 54.47 km2. The areas at greatest risk of future subsidence are Chengguan District and Qilihe District. The city contains a total of 51,163 buildings in the very high-risk area, including about 44.57% of brick-and-timber houses, 51.36% of old housing, and 52.78% of super-tall buildings, which are at especially high risk of subsidence, threatening the lives and properties of the population. The deformation results reveal poor building safety in Lanzhou, providing an essential basis for future urban development and construction.

Multi-sensor InSAR time series fusion for long-term land subsidence monitoring

ABSTRACT Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors. This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets, to address cases when partial overlaps and/or temporal gaps exist. A new Power Exponential Knothe Model (PEKM) fits and fuses overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses any temporal gaps in the series. Taking the city of Wuhan (China) as experiment area, COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019) and Sentinel-1 (2019–2021) SAR datasets were fused to map long-term surface deformation over the last decade. An independent 2011–2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison. The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The correlation coefficient of the overall results is 0.78, which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve. The experimental results are consistent with existing studies of surface deformation at Wuhan, demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.

Laboratory development of TDR automatic distributed settlement sensing for land subsidence monitoring

Recognizing land subsidence to reduce the risk is critical in complex alluvial-basin aquifers and the subsidence profiling requires multi-leveling techniques. However, the manual magnetic ring sampling is limited with low temporal resolution. This study adopted Time Domain Reflectometry (TDR) for automatic and high-accuracy instrumentation with designs of subsidence sensor and data reductions. A physical model was included to examine measurement accuracy and standard deviation due to the groundwater variation. The water fluctuates in the unconfined aquifer scenario affected the measurement accuracy to −1.52 cm/m by the Maximum point method. Additionally, the Maximum point accuracy is 0.84 mm/m with a stable water level as the confined aquifer case, proofing the measurement feasibility but limited measurement range due to signal attenuation. Thus, a novel TDR multi-sections schema was provided to solve the signal attenuation, resulting in the measurement range extending to 150 m with a standard deviation ≤ 1 mm.

Research on Monitoring, Early Warning and Prevention System of land Subsidence in Beijing-Tianjin-Hebei Region

The Beijing-Tianjin-Hebei region, one of the most serious areas of ground subsidence in China, has entered the stage of high-speed development of synergistic integration, and the establishment of a unified and efficient, automatic and intelligent, mature and advanced monitoring and warning prevention system is inevitable. After reviewing a large amount of literature, we analyze and summarize that the ground subsidence in this region was formed earlier, and although the monitoring, early warning and prevention work is gradually promoted, there are still problems such as imperfect monitoring network, in depth early warning research and unsound joint prevention and control mechanism. Only with the integration of multi-source monitoring technology, increasingly refined early warning technology and multi-pronged prevention and control technology can the ground subsidence monitoring and early warning prevention and control system in Beijing, Tianjin and Hebei make great progress and gradually advance towards intelligence, refinement and wisdom.

Review on the Use of Satellite-Based Radar Interferometry for Monitoring Mining Subsidence in Urban Areas and Demographic Indicators Assessment

Abstract Mining activities represent one of the main causes leading to subsidence in the natural and urban environment. Sustainable urban planning and detection of potential hazards in mining areas involve the use of adequate instruments such as the continuous monitoring of land subsidence. The complexity of urban environment demands the utilization of new methods for monitoring and quantifying the effects of the mining processes. In the last decades, considering the technological developments from the remote sensing domain, the Synthetic Aperture Radar Interferometry (InSAR) techniques offer the opportunity for early detection and continuous monitoring of subsidence in mining areas, including urban centers. Considering various parameters of mining subsidence monitoring, a review of several tens of studies realized in different mining sites, based on InSAR techniques, is presented. As mining subsidence in urban areas has a direct impact on the quality of life, the review is completed with demographic indicators assessment, followed by a study case on the dynamics of the population in an urban mining area from Romania, Ocnele Mari.

Monitoring Land Subsidence along the Subways in Shanghai on the Basis of Time-Series InSAR

In recent years, Shanghai has entered a stage of microscale land subsidence, but the uneven subsidence is still significant, with long-term impacts on the operational safety of subways and other infrastructures. On the basis of 154 high-resolution Terra Synthetic Aperture Radar-X (TerraSAR-X) images captured from 2013 to 2020 and the time-series persistent scatterer-interferometric SAR (PS-InSAR) method, the land subsidence along the subways in Shanghai was acquired, and the levelling data of 56 benchmarks were used to validate the measurements derived by PS-InSAR. The results indicated that the two data sets agreed well, with a correlation coefficient of 0.9 and maximum D-value of 4.0 mm derived from six pairs of comparative data sequences. The proportion of PS points showing deformation rates between −3.0 mm/a and 3.0 mm/a reached 99.4%. These results indicated that the land subsidence trend along the subway was relatively stable overall, while significant deformation was distributed mainly along the suburban subways, especially the lines that were newly open to traffic, such as Line 5 and the Pujiang line (PJ Line); along these lines, the proportions of PS points with deformation rates exceeding ±3 mm/a were 7.2% and 7.6%, respectively, and the proportions were much smaller in the other lines. The maximum cumulative deformation (MCD) along the subways was located between Jiangchuan Road Station and Xidu Station of Line 5 with a value of −66.4 mm, while the second and third MCDs were −48.2 mm along Line 16 and −44.5 mm along PJ Line, respectively. Engineering constructions, such as human-induced ground loads, foundation pit constructions, and road constructions, were the main factors affecting local land subsidence. The analysis results also showed that land subsidence was relatively significant during the period before the subways were open to traffic due to subway construction, while land subsidence clearly slowed after the subway lines were open to traffic. This deceleration in land subsidence was closely related to the rise in the groundwater level.

Toward sustainable inland aquaculture: Coastal subsidence monitoring in Taiwan

Inland aquaculture is a major industry that provides fish to reduce the stress of oceans in supplying global protein consumption. Developed and developing nations house their inland aquaculture industries largely in coastal areas, where groundwater is pumped to supply the needed water in a non-sustainable way that leads to coastal land subsidence. This study shows a comprehensive picture of land subsidence in Pingtung, a coastal plain in southern Taiwan, where, after the gradual abandonment of rice cultivation, huge groundwater exploitation has occurred since the 1970s to feed inland aquaculture. Integrated interpretation of multidevice monitoring results has improved knowledge of land subsidence associated with land uses and groundwater pumping. Results indicate a maximum cumulative subsidence of 350 cm from 1972 to 2019 near the aquaculture-rich township of Linbian. Sub-surface displacement measurements using multilayer compaction wells identified the de-watered layers responsible for the land subsidence in four sinking-affected coastal areas, where different groundwater usage patterns lead to different patterns of aquifer-system compactions at varying depths. The use of SAR interferometry with high spatial resolution and frequent revisit times has made it possible to overcome the inherent limitations of ground-based measurement methodologies such as leveling, continuous global navigation satellite system, and multilayer compaction wells, which while very accurate do not allow for adequate monitoring of subsidence in the Pingtung coastland due to the high dynamics and spatial variability of the process. Based on the results of this study, several sustainable aquaculture-related actions are being implemented or have been proposed to reduce the risks of land subsidence, flooding, and salinization in Pingtung. These include the use of InSAR monitoring for conventional sensors to continuously and timely monitor land subsidence.

Seasonal Subsidence and Heave Recorded by Borehole Extensometers in Houston

Seasonal ground movements, particularly subsidence and heave, present a great challenge for the design of roadways, parking lots, underground utility lines, and buried culverts. The US Geological Survey has been operating 13 deep borehole extensometers in Houston, Texas, for land subsidence monitoring since the 1970s and the early 1980s. The half-century extensometer datasets provide direct and continuous measurements of seasonal ground movements in the vertical direction. The Houston global positioning system (GPS) network (HoustonNet) provides independent measures for the timely evaluation of extensometer data. According to this study, shallow expansive soils can induce up to 4 cm (peak-to-trough) seasonal subsidence and heave, and deep sediments can further produce up to 2 cm seasonal subsidence and heave. The latter is primarily caused by the seasonal fluctuations of hydraulic heads in the lower Chicot and upper-Evangeline aquifers, varying considerably over time and space. The results from this study are important for determining potential vertical movement (PVM) for the design of pavements and shallow foundations and for the optimization of groundwater management in the Houston region.

Research on prevention and control methods of land subsidence induced by groundwater overexploitation based on three-dimensional fluid solid coupling model—a case study of Guangrao County

Land subsidence is an environmental geological phenomenon with slowly decreasing ground elevation, The North China Plain is one of the areas with the most serious land subsidence in China, and Guangrao County is one of the subsidence centers. This paper is based on the hydrogeological and engineering geological data of Guangrao County, the groundwater monitoring data for many years and the land subsidence monitoring data, systematically analyzes the dynamic characteristics of groundwater, the distribution and evolution of land subsidence, and the correlation between groundwater exploitation and land subsidence development in different layers of this area. Based on Biot porous medium consolidation theory, establishes a three-dimensional fluid solid coupling numerical model of land subsidence in Guangrao County, restores the development process of land subsidence, predicts and analyzes the subsidence evolution law under different groundwater exploitation schemes, and proposes targeted prevention and control measures. The research results show that: the shallow groundwater forms a cone of depression with Guangbei Salt Field as the center, and the deep groundwater forms an elliptical regional cone of depression with the urban area as the center. The ground is gradually formed two small settlement areas with the urban area of Guangrao County and Guangbei Salt Field as the settlement center, and there is a trend of interrelated expansion. The three-dimensional fluid solid coupling model of land subsidence accurately restored the development process of land subsidence in the study area, predicted that under the current groundwater exploitation conditions, by 2040, the settlement of Guangrao urban settlement center will increase to 1,350 mm, forming a large regional funnel centered around the urban area, and gradually developing and expanding around. Prohibition of groundwater exploitation in the main funnel area is a more reasonable and effective exploitation plan to prevent the development of land subsidence.

InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression

Global sea-level rise due to climate change is a critical problem for coastal cities. One of the coastal cities in Indonesia, Semarang, is in danger of being submerged by seawater due to flooding, which is exacerbated by land subsidence. Time-series analysis based on persistent scatterer (PS) interferometry usually suffers from sparse measurement points (MP), and thus does not completely cover the study area with low coherence. In this study, we utilized improved combined scatterers interferometry with optimized point scatterers (ICOPS) to increase the MP density. We also compared the optimization of the ICOPS method based on support vector regression (SVR) with convolutional neural network (CNN) algorithms. The ICOPS-CNN showed better model performance and MP density, and was less time-consuming. Further land subsidence analysis was conducted in Semarang using susceptibility mapping. The land subsidence susceptibility map from the standalone deep learning (DL) algorithm (CNN = 0.833) outperformed the standalone machine learning (ML) algorithm (SVR = 0.820). The hybrid DL algorithm with grey wolf optimizer (GWO) (CNN-GWO = 0.907) exhibited the highest accuracy, followed by the hybrid ML algorithm (SVR-GWO = 0.843). The accuracy was consistent with the comparison between the susceptibility maps and the ICOPS measurements, resulting in a susceptibility map based on CNN-GWO with the highest accuracy (0.889). Land subsidence monitoring using ICOPS based on CNN and susceptibility mapping based on a hybrid DL algorithm (CNN-GWO) can be used by local governments to improve urban development planning in Semarang and its surrounding areas.

Monitoring land subsidence with the combination of persistent scatterer interferometry techniques and distributed fiber optic sensing techniques: a case study in Suzhou, China

Land subsidence is a global geo-hazard caused by various natural and human factors, and it directly threatens the safety of the environment and infrastructures. Investigating the mechanism of land subsidence is becoming more and more important. In this paper, we use the persistent scatterer interferometry (PSI) technique combining the distributed fiber optic sensing (DFOS) technique to detect the spatial–temporal distributions of land subsidence and investigate the stratum deformation characteristics in Shengze, Suzhou. By analyzing Sentinel-1A date between 2017 and 2019 with the PSI technique and the deformation date derived by the DFOS technique, we conclude that the land surface deformations are mostly affected by the transitional exploitation of groundwater. The average line-of-sight (LOS) deformation rate is mostly concentrated in − 3 to 2.2 mm/yr, and a maximum subsidence rate of up to − 16.9 mm/yr is observed in four industrial areas. Meanwhile, the DFOS-derived results reveal that the compression strata are mostly concentrated in 41.2–137.9 m depth, which is closely associated with the pore water pressure in the second confined aquifer. And it also reveals that the groundwater over-exploitation may be the significant triggering factor of the subsidence in the study area. The InSAR-derived results are also evaluated by the deformation time series obtained by the DFOS technique. The combination of those two new sensing and monitoring technologies enables us to highlight the large deformation area and reveal the mechanism of its subsidence, which is conducive to urban development, disaster risk management, and rational exploitation and management of groundwater in Suzhou, China.

A Preliminary Result of Land subsidence Monitoring by Using Low-Cost GNSS in Bandung Basin

Land Subsidence is one of the issues faced by major cities in Indonesia. The need for a land subsidence monitoring station that operates continuously is one of the most important aspects today. The current use of low-cost GNSS (Global Navigation Satellite System) is expected to help in dealing with this problem. This study aims to evaluate Low-Cost GNSS for monitoring land subsidence in Bandung Basin. The Measurement of Low-Cost GNSS using Chip-GNSS U-Blox (single frequency) and the microstrip antenna was assembled itself. The monitoring of land subsidence was carried out at one point(TK01) with an observation period from March 2021 to December 2021. The result of data processing shows the value of the coordinate standard deviation of the TK01 point during the measurement period ranges from ± 1.5 cm. The results of vertical component processing show the standard deviation in the vertical direction is ± 3.5 cm. The rate of land subsidence resulting from linear regression at the point of TK01 from March 2021 to December 2021 in this study was -13.9 cm/year with a standard deviation value of ±0.32 cm/year.

Monitoring Land Subsidence and its Effect on Surabaya Coastal Inundation by Utilizing GPS Campaigns Data

Land subsidence is a process of land movement based on a certain datum (geodetic reference frame) in which there are various variables causing it. This phenomenon occurs in many big cities in Indonesia, which is the city of Surabaya. The Surabaya city has a type of soil in the form of alluvial soil which is prone to movement. The accumulation of land subsidence that occurs will certainly cause its own losses. As a result, it can damage urban infrastructure so that in the future it can cause disruption to the environment, economic activities, and social life in the region. In addition, the Surabaya area is also a coastal area. Coastal areas have characteristics where this area will be strongly influenced by natural events. One of the natural events that affect the oceans and coastal areas is global temperature changes. The phenomenon of global temperature changes can affect sea level rise. If sea level rise occurs accompanied by an increase in the rate of land subsidence, this can result in flooding or inundation in coastal areas. Considering that the city of Surabaya is a metropolitan city that has a high population density and a continuous level of development, mitigation efforts are important in following up on land subsidence in this area. One of the efforts that can be applied is by conducting GPS observations. The outcomes of the GPS observations were then processed using scientific software in the form of GAMIT/GLOBK. According to the study’s findings indicated that Surabaya is experiencing land subsidence in the northern and eastern coastal areas with a range of 0.2 – 83.3 mm/year. The point that experienced a significant decline was in the border area of Tandes and Asemrowo, namely station BM35. Furthermore, land subsidence conditions influence the extent of tidal inundation, where in 2032 tidal inundation will inundate 105.411 km2.