Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate − 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is − 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.

Although the sustainability of cultural heritage plays important roles in the inheritance of humans’ civilization and history, assessments of such fields is still limited. In this paper, the spatiotemporal analysis method and coupling coordination model were applied to assess the sustainability of cultural heritage in Shandong Province. Results show that (1) the National Key Cultural Relics Protection Units (“cultural units“ hereinafter) of Shandong Province have obvious convergence of spatiotemporal and city distribution; (2) although the tourism response degree of each city vary greatly, their tourism income exhibits positive correlation (i.e., correlation coefficient reaches 0.6639) with cultural units; (3) the coordination between cultural units, capital inputs, and tourism income in different cities is different, mainly shows three levels of well coordination (Jinan, Jining, Qingdao, Yantai, Weifang, and Zibo), general coordination (Zaozhuang, Weihai, Tai’an, Liaocheng, Heze, Linyi, and Rizhao), and poor coordination (Dezhou, Binzhou, and Dongying). These findings imply that the SDGs 11.4.1 is an effective indicator to promote the sustainable development of cultural heritage. However, there are still shortcomings at the city level, and the distribution of heritage and tourism response in the area should be considered in the future.

This paper aims to address the limitations of the distribution number and uniformity of Continuously Operating Reference Stations (CORS) and their impact on the reliability of inverting regional groundwater storage (GWS) based on Green’s function method and using global navigation satellite system (GNSS) data. A fusion method on the inversion of regional GWS changes from GNSS and the Gravity Recovery and Climate Experiment (GRACE) was proposed in this paper. Taking the Shaanxi–Gansu–Ningxia (SGN) region as an example, the in situ groundwater level data from ten CORS stations and eight wells were used for test analyses. In this paper, an atmospheric pressure model from the European Centre for Medium-Range Weather Forecasts (ECMWF), a global land data assimilation system (GLDAS), a WaterGAP global hydrology model (WGHM), and mean sea level anomaly (MSLA) data were used to quantitatively monitor the influence of vertical deformation caused by non-tidal environmental load. After deducing these loading deformations from the filtered time series of non-linear monthly geodetic height from the GNSS, the GWS changes in the SGN region from 2011 to 2014 were inverted. Meanwhile, the change in surface water storage from the GLDAS and WGHM models were removed from the terrestrial water storage (TWS) changes derived from GRACE. On this basis, the remove–restore theory in the Earth’s gravity field was introduced to both fuse the inversion results and obtain the regional GWS changes based on the fusion method. The results showed the following: (1) The local characteristics from the fusion results were more prominent than those of GRACE on the spatial scale, such as in the southwest and northeast in the study area. In addition, the fusion results were more uniform than those from GNSS, especially for the sparse and missing areas in which CORS stations were located, and the local effect was weakened. (2) On the time scale, compared with GRACE, the trends in GWS changes obtained from the fusion method and from GNSS inversion were roughly the same as the in situ groundwater level changes. (3) For the in situ groundwater wells “6105010031” and “6101260010”, the correlation coefficients of the fusion result were 0.53 and 0.56, respectively. The accuracy of the fusion method was slightly higher than that from GNSS, which indicates that the fusion method may be more effective for areas where CORS stations are missing or sparsely distributed. The methods in this paper can provide significant reference material for hydrodynamic research, sustainable management of water resources, and the dynamic maintenance of height data.

It is well-known that structures composed of super high-rise buildings accumulate damages gradually due to ultra-long loads, material aging, and component defects. Thus, the bearing capacity of the structures can be significantly decreased. In addition, these effects may cause inestimable life and property losses upon strong winds, earthquakes, and other heavy loads. Hence, it is necessary to develop real-time health monitoring methods for super high-rise buildings to deeply understand the running state during operation, timely discover potential safety potentials, and to provide reference data for reinforcement design. Along these lines, in this work, the built super high-rise buildings (Yunding Building) and super high-rise buildings (the Main Tower of the Shandong International Financial Center), under construction, were selected as the research objects. The overall dynamic deformation laws of super high-rise buildings were monitored by using ground-based real aperture radar (GB-RAR) technology for its advantages in non-contact measurement, remote monitoring, and real-time display of observation results. Denoising of the observation data was also carried out based on wavelet analysis. The visualization of the space state of the Yunding Building was realized based on handheld LiDAR technology. From the acquired results, it was demonstrated that the measuring accuracy of GB-RAR could reach the submillimeter level, while the noises under a natural state of wavelet analysis were eliminated well. The maximum deformation values of the Yunding Building and the Main Tower of Shandong International Financial Center under their natural state were 9.63 mm and 16.46 mm, respectively. Under sudden wind loads, the maximum deformation of the Yunding Building could be as high as 895.79 mm. The overall motion state switched between an S-shaped pattern, hyperbolic-type, and oblique line, presented the characteristics of nonlinear elastic deformation.

During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes.

MSBAS InSAR (Multidimensional Small Baseline Subset Interferometric Synthetic Aperture Radar) is a multidimensional deformation time series inversion method that integrates multi-orbit SAR data. Due to plenty of observations and the quite strong multicollinearity between the column vectors of the coefficient matrix, the MSBAS InSAR deformation observation model has serious ill-posed problems. Tikhonov regularization method is a common method for solving the ill-posed problem of geodetic measurement. Although the regularization matrix constructed by the existing methods can achieve selective correction of singular value, the non-negative definite property of the matrix is often neglected. Therefore, an exponential function singular value correction regularization method is proposed to ensure the non-negative definite characteristic of the regularization matrix. In addition, an exponential function regulatory factor is introduced to ensure that the value of the regularization matrix is within a reasonable range. The rationality of the exponential function regulatory factor is analyzed theoretically, and a method of determining the regulatory factor in the sense of minimum mean square error is proposed. The exponential function singular value correction regularization method is applied to the MSBAS InSAR deformation inversion in a mining area, and the effectiveness of the method is verified by the leveling results in the study area. The results show that the proposed method can not only effectively improve the ill-posed problems of the MSBAS InSAR deformation inversion, but also reduce the visual ambiguity caused by the single line of sight, and improve the accuracy of the results by 28% compared with the results of widely used truncated singular value decomposition method.