Land subsidence modelling using a long short-term memory algorithm based on time-series datasets

Li, Huijun; Zhu, Lin; Gong, Huili; Sun, Hanrui; Yu, Jie

doi:10.5194/piahs-382-505-2020

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Therefore, the monitoring of ground surface displacements is used to model the land subsidence associated with rock mass drainage. Many geomechanical [104][105][106][107][108][109], stochastic [110][111][112][113][114][115][116][117][118], GIS-based analysis [119][120][121][122][123][124][125][126][127][128][129] and, more recently, AI (Artificial Intelligence)based models [130][131][132][133] can accurately predict mining-induced ground movements. The influence function method is, however, most widely used [112].…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Guzy

Witkowski

2021

Energies

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

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Section: Introductionmentioning

confidence: 99%

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Guzy

Witkowski

2021

Energies

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“…The LSTM model is a common prediction model for land subsidence, which has a strong ability to mine the nonlinear characteristics of geographical phenomena along with model time evolution and, thus, it has been extensively used in geosciences, including land subsidence, hydrology, and ecology [52]. The basic unit of the LSTM is composed of three gates (Figure 5).…”

Section: Lstm-tcn Modelmentioning

confidence: 99%

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

et al. 2023

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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.

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“…In contrast, data-driven models (such as the grey model (GM) and modified GM) are based on time series data and data mining, and short-term simulations are then output. Although these models provide good results, they have difficulty providing long-term simulations for prevention and control [28].…”

Section: Introductionmentioning

confidence: 99%

Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

Zhu

Lin

et al. 2023

Remote Sensing

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Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.

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Land subsidence modelling using a long short-term memory algorithm based on time-series datasets

Cited by 11 publications

References 10 publications

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

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

Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

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