Both natural and anthropogenic factors control surface water and groundwater chemistry and quality in the Ningtiaota coalfield of Ordos Basin, Northwestern China

Yang, Yina; Mei, Aoshuang; Gao, Shi; Zhao, Di

doi:10.1007/s11356-023-27147-2

Cited by 5 publications

(1 citation statement)

References 87 publications

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“…Groundwater plays an important role during the mining activities, which is capable of amplifying the microdynamics of the geological setting caused by mining activities. In addition, mine water inrush induced by the mining activities threatens the mine safety [9,10]. Thus, monitoring and predicting the dynamic change in groundwater level is the most effective way to prevent the secondary disaster of mining activities.…”

Section: Introductionmentioning

confidence: 99%

Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China

Luo,

Dong,

Wang

et al. 2024

Water

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As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities.

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Section: Introductionmentioning

confidence: 99%