Topographic relief is a key factor limiting population distribution and economic development in mountainous areas, especially in the transition zone from mountains to plains. Taking the southern Anhui mountainous area as an example, based on the digital elevation model (DEM) with a resolution of 30 m, we used ‘quadratic’ mean change-point analysis to calculate the optimal statistical unit, and then extracted the topographic relief. Taking the county as the unit of analysis, two indicators of population density and economic density were selected. Spatial statistics and correlation analysis were used to quantitatively analyze the impact of topographic relief on population and economy. Finally, the impact of slope and elevation was analyzed. The following results were obtained. (1) The topography of the study area was dominated by medium relief (200–500 m), followed by small relief (70–200 m), flat (0–30 m), and slight relief (30–70 m), and a small proportion of large relief (≥500 m). (2) The impact of topographic relief on population and economy was slightly stronger than that of slope and elevation. The impact on population distribution was stronger than that on economic development. The impact on primary industry was stronger than that on secondary and tertiary industries. (3) In the southern Anhui mountainous area, 72.35% of the population and 76.72% of GDP were distributed in the area with a topographic relief of 155 m or less, while the land area only accounted for 43.93%. The area with a topographic relief greater than 245 m accounted for 28.76%, but only 10.69% of the population, and only 8.34% of GDP. The population distribution and economic development were obviously concentrated in the low topographic relief area. However, the characteristics of high topographic relief not only hindered the agricultural mechanization and limited the development of the primary industry, but also had a significant impact on infrastructure development, investment, and industrial layout, thus weakening regional economic advantages. In the future, the economic level of these areas needs to be improved.

Accurately forecasting the scope of coal mining subsidence area is of great significance to the protection of surface structures. Due to the peculiarities of the unconsolidated layers rock formation, the surface movement of the thick unconsolidated layers mining area converges slowly at the boundary of the basin, and the boundary of the subsidence basin is larger than under conventional conditions. It is found that the existing models have a poor prediction effect at the boundary, and the predicted subsidence basin range is smaller than the actual basin range. To solve this problem, a new surface deformation prediction model based on Boltzmann function (IB) is proposed in this paper. Aiming at the problem that the model function is highly nonlinear and difficult to obtain parameters, the multi-population genetic algorithm (MPGA) is introduced into the parameter solution of the prediction model, and the parameter calculation model based on multi-population genetic algorithm (MPGAIB) is constructed. The simulation experiment and engineering example analysis show that both the overall fitting effect and the fitting effect at the boundary of IB model are closer to the actual situation, The MPGAIB model has good ability to anti-random error and gross error, and the result is stable.

Surface deformation of mining area caused by mining subsidence can be divided into three stages: the initial stage, the active stage and the decline stage. At the active stage, D-InSAR technology could be easily affected by the incoherence of large-gradient deformation, thus leading to the failure of conventional D-InSAR technology in monitoring mining subsidence.In order to solve the problem, this paper developed a 3D surface deformation prediction method of mining subsidence by integrating dynamic Boltzmann prediction model with D-InSAR. It was firstly to monitor the surface movement and deformation by D-InSAR technology during the decline stage and obtain the surface LOS deformation field data. Then, on the basis of Boltzmann model, the geometric equation between LOS deformation by D-InSAR monitored and 3D deformation was established. Next, the geometric equation was solved based on the GA theory, and the parameters of Boltzmann prior model were obtained. On the basis, the 3D deformation of mining area were obtained, proving that D-InSAR technology was effective to get 3D deformation of mining subsidence. Through simulation experiments, the effectiveness of the proposed method is verified. The surface deformation of 1613 working face in Guqiaonan coal mine was monitored by D-InSAR during the decline stage, and the LOS deformation values were obtained. The 3D deformation was obtained from the initial stage to the decline stage by using the method of DB-InSAR. The monitoring results showed that the fitting errors in the LOS deformation were mostly within 3 mm, the RMSES was ±2.37 mm, shown that the fitting accuracy was high. The RMSE of subsidence were ±120 mm. The results showed that the method of DB-InSAR has engineering application values.

An accurate model of the Zenith Tropospheric Delay (ZTD) plays a crucial role in Global Navigation Satellite System (GNSS) precise positioning, water vapor retrieval, and meteorological research. Current empirical models (such as the GPT3 model) can only reflect the approximate change trend of ZTD but cannot accurately reflect nonlinear changes such as rapid fluctuations in ZTD. In recent years, the application of machine learning methods in the modeling and prediction of ZTD has gained prominence, yielding commendable results. Utilizing the ZTD products from 53 International GNSS Service (IGS) stations in Europe during the year 2021 as a foundational dataset, a Generalized Regression Neural Network (GRNN) is employed to model IGS ZTD while considering spatiotemporal factors and its association with GPT3 ZTD. This endeavor culminates in the development of a refined GRNN model. To verify the performance of the model, the prediction results are compared with two other ZTD values. One is obtained based on the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data, and the other is obtained by the GPT3 model. The results show that the bias of the GRNN refined model is almost 0 mm, and the average Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE) are 18.33 mm and 14.08 mm, respectively. Compared with ERA5 ZTD and GPT3 ZTD, the RMSE of GRNN ZTD has decreased by 19.5% and 63.4%, respectively, and the MAE of GRNN ZTD has decreased by 24.8% and 67.1%. Compared with the other two models, the GRNN refined model has better performance in reflecting the rapid fluctuations of ZTD. In addition, also discussed is the impact of spatial factors and time factors on modeling. The findings indicate that modeling accuracy within the central region of the modeling area surpasses that at the periphery by approximately 17.8%. The period from June to October is associated with the lowest accuracy, whereas the optimal accuracy is typically observed from January to April. The most substantial differences in accuracy were observed at station OP71 (Paris, France), with the highest accuracy recorded (9.51 mm) in April and the lowest (24.00 mm) in September.

The spatial coupling of population and economy is an important indicator that reflects regional differences and measures the coordination degree of industrial layouts and environments. To explore the spatial coupling of population and economic densities and the effect of topography in Anhui Province at a grid scale, a land-use impact model was used to spatialize socio-economic indicators in Anhui Province using 2020 county-level data. Geographical concentration and coupling index were used to quantify the spatial relationship between population and economic densities. Then the effects of topography were assessed. The results show the following: (1) the accuracy of the regression models for the individual counties is generally better than that of the models for the whole region. The population and economic densities predicted by the proposed model reflect actual conditions. (2) Topography has a stronger effect on population density and primary industry density than on secondary and tertiary industry density. Slope has the strongest effect on population and economic densities, followed by topographic relief and elevation. (3) A spatial correlation exists between topographic factors and population and economic densities. Although the spatial relationship between population and economic densities is predominantly of the balanced development type in regions with complex topography, population and economic densities are significantly lower in regions with complex rather than flat topography. In addition, economic development in the northern Anhui region, a flat region, lags behind population aggregation. Efforts should be made to improve the economic level of the southern and northern Anhui regions and the Dabie Mountain region in western Anhui.

The extraction of underground mineral sources has a significant negative impact on the local environment, results in land surface subsidence. As far as subsidence monitoring technology is concerned, leveling is the most accurate. However, leveling can only obtain discrete point data but not the whole area information of the subsidence basin. In this study, Differential Interferometric Synthetic Aperture Radar (D-InSAR) combined with Unmanned Aerial Vehicle (UAV) technology is used to study the subsidence characteristics of the whole working panel. In this analysis, the Huainan mining area is tested as a research area; measured data are compared to the elevation accuracy of the Digital Surface Model (DSM) data, which can be considered for the subsequent works of the mining area. Based on the subsidence affected area, the ground object-type information is recorded to provide basic information for the ecological restoration work after mining so that the data before and after mining can be obtained synchronously. Finally, the differential interference results and Digital Orthophoto Map (DOM) data are combined to assess the spatiotemporal evolution of working panel subsidence and its influence on surface features. The main novelty of the proposed work is combining UAV and D-InSAR to get more accurate analysis of mining subsidence. It can be done using the proposed method.