Temporal variation of groundwater hydrochemistry and water stable isotopes under long-term mining disturbance in a coal mine, northwest China

Liu, Fengxia; Wang, Guangcai; Liang, Xiangyang; Qu, Shen; Shi, Zheming; Li, Jian; Luo, Ankun

doi:10.1016/j.apgeochem.2023.105802

Cited by 7 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(4) Calculate the entropy using the normalized results. (5) The window size V that corresponds to the maximum entropy is considered the optimal window size.…”

Section: Combined Prediction Model Under Sliding Windowmentioning

confidence: 99%

“…Due to the influence of geological factors and other variables, surface movements and deformations persist for a certain duration after coal seam extraction has ceased. During this period, the impacts on groundwater continue to evolve over time [4,5]. Additionally, structures on the surface, as well as the surrounding ecological environment within the mining area, are subjected to varying degrees of influence and damage during different postextraction phases [6,7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

Chai,

Xu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

To attain precise forecasts of surface displacements and deformations in goaf areas (a void or cavity that remains underground after the extraction of mineral resources) following coal extraction, this study based on the limitations of individual time function models, conducted a thorough analysis of how the parameters of the model impact subsidence curves. Parameter estimation was conducted using the trust-region reflective algorithm (TRF), and the time function models were identified. Then we utilized a combined model approach and introduced the sliding window mechanism to assign variable weights to the model. Based on this, the combined model was used for prediction, followed by the application of this composite prediction to engineering scenarios for the dynamic forecasting of surface movements and deformations. The results indicated that, in comparison with DE, GA, PSO algorithms, the TRF exhibited superior stability and convergence. The parameter models obtained using this method demonstrated a higher level of predictive accuracy. Moreover, the predictive precision of the variable-weight time function combined model surpassed that of corresponding individual time function models. When employing six different variable-weight combination prediction models for point C22, the Weibull-MMF model demonstrated the most favorable fitting performance, featuring a root mean square error (RMSE) of 32.98 mm, a mean absolute error (MAE) of 25.66 mm, a mean absolute percentage error (MAPE) of 7.67%; the correlation coefficient R2 reached 0.99937. These metrics consistently outperformed their respective individual time function models. Additionally, in the validation process of the combined model at point C16, the residuals were notably smaller than those of individual models. This reaffirmed the accuracy and reliability of the proposed variable-weight combined model. Given that the variable-weight combination model was an evolution from individual time function models, its applicability extends to a broader range, offering valuable guidance for the dynamic prediction of surface movement and deformation in mining areas.

show abstract

“…(4) Calculate the entropy using the normalized results. (5) The window size V that corresponds to the maximum entropy is considered the optimal window size.…”

Section: Combined Prediction Model Under Sliding Windowmentioning

confidence: 99%