Coal mining disrupts soil structure and causes water loss, thereby affecting the ecological environment of mining areas. Rapid, accurate, and non-destructive detection of surface soil moisture is crucial for advancing ecological restoration in these regions. This study focuses on the mined and unmined areas of the Yushuquan coal mine, located on the southern slope of the Tianshan Mountains in Xinjiang, China. The soil volumetric water content (SVWC) was measured using time-domain reflectometry (TDR), while the shallow subsurface soil was investigated using ground-penetrating radar (GPR). Various features were extracted from GPR signals in both the time- and frequency-domains, and their relationships with SVWC were analyzed. Multiple features were selected and optimized to determine the optimal feature combination for building a multi-feature backpropagation neural network model for soil volumetric water content prediction (Muti-BP-SVWC). The performance of this model was compared with two single-feature-based methods for SVWC prediction: the average envelope amplitude (AEA) method and the frequency shift method. The application results of the Muti-BP-SVWC model in different regions demonstrated significant improvements in accuracy and stability compared to the AEA method and the frequency shift method. In the mined area validation set, the model achieved an determination coefficient (R²) of 0.77 and the root mean square error (RMSE) of 0.0091 cm³/cm³, while in the unmined area validation set, the R² of 0.84 and an RMSE of 0.0059 cm³/cm³. These results indicate that incorporating multiple features into the BP neural network can better capture the complex relationship between GPR signals and SVWC. This approach effectively inverts the shallow subsurface soil moisture in mining areas and provides valuable guidance for ecological restoration in these regions.

The groundwater system is polluted by chlorinated hydrocarbon pollution in eastern Jinan, Shandong province, China, exceeding the limit of pollutants concentration in class Ⅲ of Groundwater Quality Standards (GQS, GB/T 14848-2017). In order to improve the current situation of chlorinated hydrocarbon pollutants in groundwater system of eastern Jinan, the optimization study of the pollution control is carried out. In this paper, the pollutant of carbon tetrachloride in groundwater system is taken as the main research object. By using GMS numerical simulation software to establish the solute transport model of carbon tetrachloride. The simulation results illuminate that with the passage of time, the concentration of carbon tetrachloride decreases, but the acreage of pollution plume in groundwater still has little change, and it is hard to reach the limit standard of 2.0μg/L in class Ⅲ of GQS within a short time. Sequentially, on the basis of simulation model, the optimization of pumping and injection wells is conducted in the pollution control field of research region. The results turn out that the layout of 8 pumping and 5 injection wells has the best control effect of carbon tetrachloride in groundwater system, the pollutant concentration reached the limit standard of 2.0μg/L in only 1187d, and the acreage of pollution plume in control field reduced from 21.80km2 to 12.21km2. In summary, through the optimal pollution control scheme of carbon tetrachloride, the time for pollutant concentration to reach 2.0μg/L has been reduced, and the acreage of pollution plume has been effectively controlled. This work further investigates the promoting effect of injection wells on hydraulic control of groundwater pollution, which can accelerate the circulation of the groundwater system and save the treatment time, providing a relatively practical way for the prevention and control of chlorinated hydrocarbon pollutants.