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The surface subsidence duration and the maximum subsidence velocity are critical indicators to evaluate the stability and severity of surface damage. Precisely predicting them is important for guiding engineering design and protecting ground infrastructure. Traditional manual measurement methods are time-consuming and laborious, and the existing empirical formulas have low accuracy and poor applicability. Therefore, a new prediction method was established in this paper. Measured data from 30 mining areas were used for verification. The results show that the predicted surface subsidence duration is basically consistent with the measured value. The standard deviation of the two is 61 d, and the relative standard deviation is 6.6%. The predicted surface maximum subsidence velocity is basically consistent with the measured value. The standard deviation of the two is 10.0 mm/d, and the relative standard deviation is 1.6%. The surface subsidence duration and the maximum subsidence velocity are positively correlated with the coal seam thickness, negatively and positively correlated with the mining speed, and positively and negatively correlated with the mining depth. The mining speed and mining depth have the same sensitivity to the two indicators, and the coal seam thickness is more sensitive to the surface subsidence duration. Furthermore, construction within the subsidence basin may further contribute to surface subsidence. Therefore, land reuse measures should be implemented following the predicted surface subsidence duration in this paper. This study addresses the knowledge gap in this field by deriving theoretical formulas for surface subsidence duration and maximum subsidence velocity. In the absence of sufficient measured data, engineers can calculate predicted values in combination with geological mining conditions and develop appropriate mining plans based on the extent of surface subsidence. [ABSTRACT FROM AUTHOR]

This study uses the Shangwan coal mine in Shendong Mine as its research area and evaluates the vegetation net primary productivity (NPP)'s impact in the mining area based on the multi-scale research unit of working face. The probability integral model (PIM) was employed to analyze the characteristics of spatiotemporal variation and mining impact laws of surface vegetation NPP in the entire Shangwan coal mine and working face impact zone. We proposed vegetation NPP impact assessment scheme based on working face and annual mining impact spatiotemporal scales, as well as impact distance and duration evaluation parameters, and multi-scale evaluation results of NPP in the mining area were calculated. (1) The vegetation NPP of the Shangwan coal mine has shown a fluctuating growth trend from 2000 to 2023. The annual average NPP variation value is 98.5–280.7 gC/m2, and the average annual value is 198.8 gC/m2. (2) By analyzing the fourth district impact zone, the impact patterns of the underground mining area, subsidence area, and vegetation NPP above the mining area were revealed for each mining year. (3) From the impact of mining on the 12401 working face in 2018, the mining impact distance on surface vegetation NPP is 300–400 m, and the impact duration is 3–4 years. It reveals that the impact of underground mining on surface vegetation NPP in the entire coal mining area is not significant. The NPP in mining area shows a temporal variation pattern of fluctuating growth and stabilizing trends. The research results have comprehensively revealed the degree and characteristics of underground mining's impact on surface vegetation from different evaluation scales, providing a basis for effective management of the mining area environment. [ABSTRACT FROM AUTHOR]

The subsidence prediction method is very important for predicting the negative impact induced by coal mining.Probability integral method is an important method for mining subsidence prediction, but for irregular working face mining, its prediction accuracy needs to be improved.Aiming at this problem, the Green formula was used in this paper to transform the formula of the probability integration method, and convert the integration of the working face to the line integration of the boundary of the mining area; the boundary of working face was simplified and divided into multiple straight line segments, and each straight line segment was integrated separately; the prediction of the movement and deformation of any point on the surface and the surface subsidence basin was completed through superposition calculation; finally, an application study was carried out based on a certain example, which verified effectiveness of the method in this paper.Compared with the result of the probability integration method, the prediction accuracy of the line integral proposed in this paper was improved by 23 %.

In mining areas, the surface movement and deformation are related to the safety of production and the safety of ground buildings(structures).It is of great practical significance to regularly monitor and predict the surface movement and deformation in mining areas.Due to the long duration of surface subsidence in the mining areas, the observation data has the characteristics of long time series and nonlinear change, so there are some problems such as difficulty in the selection of modeling data interval and low prediction accuracy of GM(1, 1).In view of the above problems, this paper establishes an improved GM(1, 1) model based on the residual weighted correction, and puts forward the method of interval selection of modeling data, prediction process and evaluation standard of model accuracy.It is verified by an example that the prediction accuracy of GM(1, 1) model is better than that of GM(1, 1) model, and the method of interval selection of modeling data from stage to tail can improve the prediction accuracy.

Soil moisture is an important component of the hydrologic cycle and ecosystem functioning, and it has a significant impact on agricultural production, climate change and natural disasters. Despite the availability of machine-learning techniques for estimating soil moisture from high-resolution remote-sensing imagery, including synthetic aperture radar (SAR) data and optical remote sensing, comprehensive comparative studies of these techniques remain limited. This paper addresses this gap by systematically comparing the performance of four tree-based ensemble-learning models (random forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), and category boosting (CatBoost)) and three deep-learning models (deep neural network (DNN), convolutional neural network (CNN), and gated recurrent unit (GRU)) in terms of soil moisture estimation. Additionally, we introduce and evaluate the effectiveness of four different stacking methods for model fusion, an approach that is relatively novel in this context. Moreover, Sentinel-1 C-band dual-polarization SAR and Sentinel-2 multispectral data, as well as NASADEM and geographical code and temporal code features, are used as input variables to retrieve the soil moisture in the ShanDian River Basin in China. Our findings reveal that the tree-based ensemble-learning models outperform the deep-learning models, with LightGBM being the best individual model, while the stacking approach can further enhance the accuracy and robustness of soil moisture estimation. Moreover, the stacking all boosting classes ensemble-learning model (SABM), which integrates only boosting-type models, demonstrates superior accuracy and robustness in soil moisture estimation. The SHAP value analysis reveals that ensemble learning can utilize more complex features than deep learning. This study provides an effective method for retrieving soil moisture using machine-learning and high-resolution remote-sensing data, demonstrating the application value of SAR data and high-resolution optical remote-sensing data in soil moisture monitoring. [ABSTRACT FROM AUTHOR]

Coal plays a key role in the composition of energy production and consumption in China. The mineral is a crucial resource for human economic and social development. The surface subsidence caused by coal mining has attracted disturbance and damage to the surface ecological environments of mining areas. To quantitatively study vegetation carbon sequestration in coal mining areas, this article takes the Shendong mining area as its research object and analyzes the spatiotemporal distribution characteristics and influencing factors of vegetation net primary productivity (NPP). Based on meteorological data, remote sensing images, and land use data in the mining area, this research analyzed the vegetation NPP and its relationship with meteorological factors in the Shendong mining area from 2000 to 2023. The results show that (1) in the past 24 years, the range of NPP variation in the Shendong mining area has been 0–350 gC/m2. The overall NPP shows a fluctuating upward trend. The annual average change in NPP in the mining area is bounded by the two central dividing lines, with a lower value in the central part and a gradually increasing trend in the western and eastern parts. (2) The average vegetation NPP of the Shendong mining area over 24 years was 0–250 gC/m2; after 2011, the annual average NPP of vegetation was higher than this average level, which shows that the ecological environment of the mining area is gradually improving. Coal mining in the mining area has not caused a significant impact on the overall environment of the mining area. (3) There is a significant relationship between NPP and annual cumulative rainfall, average annual temperature, and average annual Normalized Difference Vegetation Index (NDVI). Overall, it is not significantly related to mining factors. The mining factors affect local areas and have a certain correlation. [ABSTRACT FROM AUTHOR]

Most coal resources are deposited in the form of inclined coal seams, and surface subsidence basin morphology induced by the mining of inclined coal seams is frequently skewed. The probability integral method with symmetrical distribution characteristics is widely used at present in surface subsidence prediction in coal mining in China. However, this method performs poorly when the inclined coal seam mining subsidence is predicted, and prediction accuracy decreases considerably with an increase in coal seam inclination. To solve this problem, this study first establishes three coordinate systems: a working surface rectangular coordinate system, a working face body-following coordinate system, and a surface rectangular coordinate system. Then, a random medium theory is applied to realize the superposition integral operation of the subsidence influence of unit mining in the working faces body-following coordinate system. Subsequently, the subsidence effect of a certain point on the surface is converted into the surface rectangular coordinate system. Finally, the inclined coal seam mining subsidence prediction model is constructed under the surface rectangular coordinate system. Results show that the surface subsidence caused by the mining of the inclined coal seam units conforms to the Weibull polar distribution law, and the effectiveness of the prediction model is verified through examples. The relative mean squared error of the prediction is less than 10%. The results of the study can provide theoretical and technical support for the subsidence prediction of similar mining areas. [ABSTRACT FROM AUTHOR]

It is imperative to timely and accurately predict the progressive surface subsidence caused by coal mining in the context of precision coal mining. However, the existing dynamic prediction methods that use time functions still have limitations, especially in the description of the moments of initiation and maximum subsidence velocity, which hinder their wide application. In this study, we proposed the MMF (Morgan–Mercer–Flodin) time function for predicting progressive surface subsidence based on the model assumptions and formula derivations. MMF time function can resolve the limitations in the description of the moments of initiation and maximum subsidence velocity perfectly. Afterward, we established the dynamic prediction model by combining the probability integral method with the MMF time function. Finally, using the measured subsidence data of working panel 22101 as an example, the accuracy and reliability of the dynamic prediction model was verified. The average RMSE and average relative RMSE (RRMSE) of prediction progressive subsidence using MMF time function are 46.65 mm and 4.63%, respectively. The accuracy is optimal compared with other time functions (for the average RMSE, Logistic time function is 80.57 mm, Gompertz time function is 79.77 mm, and Weibull time function is 90.61 mm; for the average RRMSE, Logistic time function is 7.66%, Gompertz time function is 7.73%, and Weibull time function is 8.62%). The results show that the method proposed in this paper can fully meet the requirements of practical engineering applications, achieve accurate dynamic prediction during the coal mining process, and provide good guidance for surface deformation and building protection. [ABSTRACT FROM AUTHOR]

The current three-dimensional (3D) deformation monitoring methods, based on the single line-of-sight (LOS) interferometric synthetic aperture radar (InSAR) technology, are constructed by combining the deformation characteristics of mining subsidence basins, which are incompletely suitable in the edge area of the subsidence basin and some large deformation gradient mines with surface uplift in the LOS direction.The 3D deformation monitoring method of InSAR combined with the surface displacement vector depression angle model (InSAR+ depression angle model) is proposed to obtain more detailed and accurate deformation information of the entire basin. This method first establishes a surface displacement vector depression angle model based on the probability integral method (PIM). The magnitude of the surface displacement vector—owing to the spatial relationship between the LOS direction and the surface displacement vector—is obtained because the horizontal movement direction field and the displacement vector depression angle field of the mining area determine the 3D directions of the surface displacement vector. Then, the PIM model is used to obtain the settlement information of the central area with a large deformation gradient. A complete subsidence basin of the mining area is received by combining the proposed method and the PIM. A total of 35 Sentinel-1A data from 31 March 2018 to 13 May 2019 and the leveling data were used to apply and analyze the accuracy of this method. The experimental results show that this method can obtain more accurate information on surface subsidence around the mining area. Moreover, the overall settlement is more consistent with the actual situation, and the monitoring ability is significantly improved compared with the InSAR and PIM. [ABSTRACT FROM AUTHOR]