The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

Atmospheric drag stands out as the predominant non-gravitational force acting on satellites in Low Earth Orbit (LEO), with altitudes below 2000 km. This drag exhibits a strong dependence on the thermospheric mass density (TMD), a parameter of vital significance in the realms of orbit determination, prediction, collision avoidance, and re-entry forecasting. A multitude of empirical TMD models have been developed, incorporating contemporary data sources, including TMD measurements obtained through onboard accelerometers on LEO satellites. This paper delves into three different TMD modelling techniques, specifically, Fourier series, spherical harmonics, and artificial neural networks (ANNs), during periods of geomagnetic quiescence. The TMD data utilised for modelling and evaluation are derived from three distinct LEO satellites: GOCE (at an altitude of approximately 250 km), CHAMP (around 400 km), and GRACE (around 500 km), spanning the years 2002 to 2013. The consistent utilisation of these TMD data sets allows for a clear performance assessment of the different modelling approaches. Subsequent research will shift its focus to TMD modelling during geomagnetic disturbances, while the present work can serve as a foundation for disentangling TMD variations stemming from geomagnetic activity. Furthermore, this study undertakes precise TMD modelling during geomagnetic quiescence using data obtained from the GRACE (at an altitude of approximately 500 km), CHAMP (around 400 km), and GOCE (roughly 250 km) satellites, covering the period from 2002 to 2013. It employs three distinct methods, namely Fourier analysis, spherical harmonics (SH) analysis, and the artificial neural network (ANN) technique, which are subsequently compared to identify the most suitable methodology for TMD modelling. Additionally, various combinations of time and coordinate representations are scrutinised within the context of TMD modelling. Our results show that the precision of low-order Fourier-based models can be enhanced by up to 10 % through the utilisation of geocentric solar magnetic coordinates. Both the Fourier- and SH-based models exhibit limitations in approximating the vertical gradient of TMD. Conversely, the ANN-based model possesses the capacity to capture vertical TMD variability without manifesting sensitivity to variations in time and coordinate inputs.

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude.

The slant total electron content (STEC) values of signal paths from global navigation satellite system satellites to observation stations are an important data source for voxel-based computerized ionosphere tomography (CIT). However, the height range of the satellite signal rays is much larger than that of the ionospheric-tomography region. Therefore, it is commonly using an empirical model to obtain a fixed proportional coefficient of the STEC of each ray that is outside the tomography region, and then eliminating the influence of the plasmasphere electron content (PEC) to CIT (CIT-FPPEC), however, it has been found that this is unreasonable and can negatively affect the accuracy of CIT. Herein, we propose an improved CIT method based on adaptive estimation of the PEC (CIT-AEPEC). Numerical experiments were conducted using Global Positioning System observation data over Europe from 8-15 May 2019, and the inversion results were compared with the electron density profiles from ionosonde data and in-situ measurements of Swarm satellites. In contrast to the CIT method without removal of the PEC (CIT-STEC) and CIT-FPPEC, statistical analysis showed that the root-mean-square error (RMSE) values between the CIT-AEPEC and ionosonde observations were improved by 34.54% and 26.78%, and the RMSE values between the CIT-AEPEC and Swarm measurements were improved by 39.77% and 36.97%, respectively. Finally, CIT-AEPEC inversion results were used to detect ionospheric perturbations during two magnetic storms from 8-15 May 2019. The effects of negative ionospheric storms and daily variations were clearly visible in peak value maps of the IED. These results reveal that the proposed method is a useful tool for research examining space weather.

Accurate hourly regional precipitation forecasting is essential for flood warning and weather-risk management, but station-scale global navigation satellite system (GNSS) precipitation models cannot fully describe the spatial evolution of rainfall between stations. This study proposes an hourly regional precipitation forecasting (HRPF) model that combines gridded GNSS-derived precipitable water vapor (PWV) and gridded precipitation with a convolutional long short-term memory (ConvLSTM) encoder–decoder network. Missing values in the hourly PWV and precipitation records from 69 GNSS-collocated meteorological stations in Taiwan Province during 2008–2012 were reconstructed using singular spectrum analysis and then resampled onto a 0.06° latitude–longitude grid using nearest-neighbor interpolation. The model uses PWV and precipitation fields from the preceding 1–6 h as two input channels and predicts the precipitation field one hour ahead. Lagged autocorrelation analysis showed strong spatiotemporal dependence in both precipitation and PWV fields, with coefficients above 0.86 and 0.85, respectively. In annual-scale external validation, the HRPF model maintained probability of detection (POD) values above 90%, false alarm rate (FAR) values below 55%, and threat score (TS) values of 46.22–46.58%. Seasonal external validation showed POD values of 88.8–97.8%, with the highest TS in summer (52.8–53.3%) owing to lower FAR values (45.8–46.4%). These results indicate that GNSS-derived PWV can provide useful spatial water-vapor information for one-hour-ahead regional precipitation forecasting, while model skill remains season-dependent over Taiwan’s complex terrain.

Accurate modeling of topside ionospheric electron density is essential for improving GNSS positioning and understanding upper-atmosphere dynamics. A new four-dimensional (spatial and temporal) topside electron density model is developed using global GNSS radio occultation data within an L2-regularized artificial neural network framework. The model combines both empirical and physical variables, including geomagnetic coordinates, temporal parameters, solar flux ($F_{10.7}$), geomagnetic activity index ($Kp$), and key ionospheric parameters (NmF2 and hmF2). To support the modeling framework, two sub-models are first constructed to estimate NmF2 and hmF2 when direct measurements are unavailable. The full model is trained using COSMIC-1 data and evaluated against independent datasets, including COSMIC-1, GRACE, and incoherent scatter radar (ISR). The results show that the proposed sub-models reduce relative errors by 4.5% for hmF2 and 11.0% for NmF2 compared with IRI-2016. For the full topside $N_e$ modeling, the proposed approach achieves improvements of 35%, 36%, and 53% relative to IRI-2016 when evaluated against COSMIC-1, GRACE, and ISR datasets, respectively. A systematic analysis of input variables further indicates that both physical drivers and ionospheric structural parameters play essential roles in determining model performance. The new model incorporated with NmF2 and hmF2 sub-models still achieves a 16% improvement over IRI-2016 based on ISR data. In addition to statistical improvements, the model reproduces key ionospheric features, including the equatorial ionization anomaly (EIA) and the midlatitude summer nighttime anomaly (MSNA), under different solar activity conditions. These results demonstrate that the proposed model captures not only the statistical variability but also the underlying physical behavior of the topside ionosphere.