No abstract

A novel approach was developed to quantitatively examine land subsidence. It combines a new geophysical (NG) prospecting and the interferometric synthetic aperture radar (InSAR) technology to explore uneven development of land subsidence in Beijing, China. We derived land subsidence spatial information over 4 years (from November 2014 to July 2018) based on Sentinel‐1 satellite imagery and the small‐baseline InSAR (SBAS‐InSAR) method. Also, profile data were acquired using seismic frequency resonance (SFR) approach in a few settlement areas. We developed a geological model based on boreholes and SFR data. Thus, we can quantitively study the driving forces of a typical uneven land subsidence. We found that faults are controlling spatial developments of land subsidence in Beijing. The subsidence rates have different values along the same fault. Also, we revealed the contributions of compressible layers to the formation of uneven land subsidence.

The northeastern part of the Ordos Basin is the main recharge area of regional groundwater, the groundwater resources are relatively scarce. The main water supply source in the area is shallow groundwater, and there are many industrial and mining enterprises in the district. The potential groundwater pollution risk is high, and the the shallow groundwater vulnerability evaluation in the region is of great significance for groundwater resources protection.The weight of each indicator of the traditional DRASTIC model is fixed and does not change with the regional conditions, which may cause deviations in the evaluation results. This time, based on the DRASTIC model, the entropy weight coefficient method is introduced to determine the index weight, and the DRASTIC entropy weight model is established to obtain a more scientific and close to the actual conditions of the study area, and provide an important reference and basis for the protection of regional groundwater resources.

The ecological environment of Northeastern Ordos Basin which located in the Northwestern China belongs to arid and semi-arid area. The hazards of water and soil loss here are serious caused by gully river erosion. Since Soil erosion is an important factor leading to the destruction of regional ecology and has a profound impact on the long-term and healthy development of the economy and society, it is necessary to do research on the sensitivity analysis about soil erosion. In this study, the AHP (Analytic Hierarchy Process) method was used to determine the index weight of the factors for soil erosion, and made it more consistent with the whole regional condition. The results demonstrated that the soil medium, slope and vegetation coverage had the highest influence on soil erosion under the same climatic and meteorological conditions in the study area. This research evaluated the sensitivity factors and provided a significant guidance for soil erosion control.

Dynamic multi-attribute group decision-making (DMAGDM) is a widespread practice in which evaluations are provided by multiple decision-makers at various times and early evaluations impact later evaluations. Additionally, attributes and alternatives can be added or removed over time. An R-numbers DMAGDM method is developed based on the advantages of R-numbers in capturing risks. This paper introduces the R-numbers Einstein weighted averaging (RNEWA) operator and R-numbers weighted Einstein geometric (RNEWG) operator, which are distinct from conventional algebraic operations, and examines their properties. Moreover, an expert weight determination model is constructed using the similarity measure of R-numbers. The attribute weight determination model in the R-numbers environment is also proposed with the method based on the criteria removal effects method (MEREC). A static rating calculation model, which utilizes the combination compromise solution (CoCoSo) method in the R-numbers environment, is built using the RNEWA operator and RNEWG operator. Furthermore, a new dynamic rating calculation model is proposed which does not require storage of all decision information over time. Finally, the applicability and effectiveness of the R-numbers DMAGDM method is demonstrated through a case study on supply chain risk assessment of manufacturing enterprises.