2018
DOI: 10.1007/s12517-018-4071-8
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A GIS-based method of risk assessment on no. 11 coal-floor water inrush from Ordovician limestone in Hancheng mining area, China

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Cited by 19 publications
(11 citation statements)
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“…In the environmental thematic group can be defined the second approach, where the environmental impact on mining operation performance was investigated. In this area, research works were focused on seismic risk assessment (e.g., [109,110]) and water risk assessment (water inrush problems [111][112][113], water resources in mining [114], and surface water contamination [115]).…”
Section: Thematic Analysis Of the Conducted Reviewmentioning
confidence: 99%
“…In the environmental thematic group can be defined the second approach, where the environmental impact on mining operation performance was investigated. In this area, research works were focused on seismic risk assessment (e.g., [109,110]) and water risk assessment (water inrush problems [111][112][113], water resources in mining [114], and surface water contamination [115]).…”
Section: Thematic Analysis Of the Conducted Reviewmentioning
confidence: 99%
“…For example, the AHP-type vulnerability index method was used to partition and evaluate the risk of water inrush No. 11 coal seam floor from underlying confined aquifer in the Hancheng Coal Mine and the assessment results had high accuracy (Dai et al 2018). An optimized AHP-type vulnerability index method was used to partition and evaluate the risk of water inrush of the No.…”
Section: Introductionmentioning
confidence: 92%
“…Based on the above reasons, from the beginning of the 20th century to the present, some scholars have adopted the geographic information systems (GIS) with linear or nonlinear integration technology and proposed the water inrush evaluation method based on multisource information fusion technology. For example, Dai et al analyzed the water inrush risk of the 11th coal seam in Hancheng mining area using GIS and analytic hierarchy process (AHP) [13]. Wu et al proposed a karst water inrush water risk prediction method based on GIS and artificial neural network (ANN) coupling technology [14].…”
Section: Introductionmentioning
confidence: 99%