Abstract
Groundwater inrushes often occur in the coal mines of China. One of the water sources is the aquifers underlying the coal seams. Because such a water hazard is affected by many factors, data collected from various sources need to be evaluated to predict its occurrence. This paper introduces an innovative approach in which the water inrush risk is represented by the vulnerability index. This method combines the geographic information system and the artificial neural network. The artificial neural network is used to estimate the weight of each factor. Unlike the traditional prediction method in which two controlling factors are often evaluated without regard to their relative importance, this new approach incorporates multi-factors and describes the non-linear dynamical processes.
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Acknowledgments
This research was sponsored by China National Natural Science Foundation (grant number 40572149), the Key Projects of Ministry of Education of P. R. China (grant number 2004-295), the “973” Project (grant number 2006CB202205), and the “115” National Science and Technology Support Project (grant number 2006BAB16B04).
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Wu, Q., Zhou, W. Prediction of groundwater inrush into coal mines from aquifers underlying the coal seams in China: vulnerability index method and its construction. Environ Geol 56, 245–254 (2008). https://doi.org/10.1007/s00254-007-1160-5
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DOI: https://doi.org/10.1007/s00254-007-1160-5