Abstract
Groundwater inrush is a geohazard that can significantly impact safe operations of the coal mines in China. Its occurrence is controlled by many factors and processes are often not amenable to mathematical expressions. To evaluate the water inrush risk, Professor Wu and his colleagues have proposed the vulnerability index approach by coupling the artificial neural network (ANN) and geographic information system (GIS). The detailed procedures of using this innovative approach are shown in a case study. Firstly, the powerful spatial data analysis functions of GIS was used to establish the thematic layer of each of the main factors that control the water inrush, and then to choose the training sample on the thematic layer with the ANN-BP Arithmetic. Secondly, the ANN evaluation model of the water inrush was established to determine the threshold value for each risk level with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into four regions with different vulnerability levels and they served as the general guidelines for the mine operations.
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Acknowledgments
This research was supported by China National Natural Science Foundation (grant #40572149, 40772162), the Key Projects of Ministry of Education of P.R. China (grant #2004-295), National Key Project of Scientific and Technical Supporting Programs(2007BAK24B01, 2006BAB16B04) and the “973” Project (grant #2006CB202205).
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Wu, Q., Zhou, W., Wang, J. et al. Prediction of groundwater inrush into coal mines from aquifers underlying the coal seams in China: application of vulnerability index method to Zhangcun Coal Mine, China. Environ Geol 57, 1187–1195 (2009). https://doi.org/10.1007/s00254-008-1415-9
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DOI: https://doi.org/10.1007/s00254-008-1415-9