Deciphering the mechanism of groundwater temperature changes associated with longwall mining in a coalfield, China, using the extreme gradient boosting methodAnalyse du mécanisme des changements de température des eaux souterraines dans une exploitation minière par longues tailles d’un bassin houiller en Chine, par la méthode de l’algorithme de l’extrem gradient boostingEl mecanismo de los cambios de temperatura de las aguas subterráneas asociados a la explotación minera de tajo largo en una cuenca carbonífera de China mediante el método de incremento de gradiente extremo利用极端梯度提升算法解译中国一个煤田长壁式采矿下地下水温度变化机理Decifrando o mecanismo das mudanças de temperatura das águas subterrâneas associadas à mineração longwall em uma jazida de carvão, China, usando o método de aumento de gradiente extremo

被引:0
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作者
Shen Qu
Guangcai Wang
Shouchuan Zhang
Zheming Shi
Xiangyang Liang
Ankun Luo
机构
[1] Inner Mongolia University,Inner Mongolia Key Laboratory of River and Lake Ecology, School of Ecology and Environment
[2] China University of Geosciences,State Key Laboratory of Biogeology and Environmental Geology & MOE Key Laboratory of Groundwater Circulation and Environmental Evolution
[3] Chinese Academy of Geological Science,undefined
[4] Xi’an Research Institute of China Coal Technology & Engineering Group Corp,undefined
关键词
Thermal conditions; Machine learning; Groundwater monitoring; Mining; China;
D O I
10.1007/s10040-024-02807-w
中图分类号
学科分类号
摘要
Investigations focusing on the impacts of mining on groundwater systems typically provide a qualitative analysis of groundwater flow and chemistry, whereas relatively few studies quantitatively analyze groundwater temperature perturbations induced by mining. This study aims to identify the hydrogeological mechanism responsible for changes to groundwater temperature associated with longwall coal mining. Here, the extreme gradient boosting (XGBoost) method was used to construct three models at different phases of mining disturbance to identify the factors governing groundwater temperature dynamics: (1) a pre-disturbance model; (2) an in-disturbance model; and (3) a post-disturbance model. The feature relative importance (FRI) of input variables contributing to groundwater temperature dynamics was quantified for a long-term groundwater monitoring dataset collected from the Ningtiaota Coalfield, Ordos Basin, China. Pre-mining disturbance groundwater temperatures were stable, and the XGBoost model identified the groundwater level of the respective monitoring wells to be the greatest predictor for variation in groundwater temperature. During mining disturbance, proximal monitoring wells exhibited a decline in groundwater temperature, where the FRI of groundwater temperature in an upgradient monitoring well increased by 151–662% relative to the pre-mining disturbance model. The monitoring of aquifer properties and stable isotope composition of groundwaters provided additional evidence to suggest groundwater temperature decreases were associated with increased recharge contributions from surficial Quaternary aquifers. Post-mining disturbance, groundwater temperature and aquifer specific storage demonstrated recovered to pre-mining conditions. This study provides insights into mining-induced groundwater temperature dynamics as a result of changes to hydraulic connection between aquifers.
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页码:1419 / 1432
页数:13
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