Abstract: In an attempt to explore the influence of coal mining and karst water exploitation on Jinci spring, the discharges in spring area are simulated by multiple linear regression model and back propagation (BP) neural network model under two scenarios (in the absence of karst water exploitation and in the absence of coal mining drainage, respectively) for comparison. In line with the study area’s driving factors, the Jinci spring area is generalized as a multi-input system with dual output. Precipitation, karst water exploitation amount, and coal mining drainage amount in the current year and previous seven years are selected as input; the total discharge of Jinci spring, inclusive of outflow and side discharge, is taken as output. The relations between each input and output are established. Under the annual average conditions of 1956-1994, 1) the results of multiple linear regression model manifest that karst water exploitation had reduced the discharges of Jinci spring area by 0.42 m³/s, coal mining had cut the outflow by 0.23 m³/s, in total of 0.65 m³/s; 2) while the results of BP neural network model shows that karst water exploitation reduced the discharges by 0.30 m³/s,coal mining by 0.27 m³/s,and the total influence was 0.65 m³/s, which was nonlinear. Both stochastic models reflected severe impact of coal mining on Jinci spring area since coal mining and karst water exploitation exacerbated after the 1980s.

Key words: total discharge of Jinci spring, multiple linear regression, BP artificial neural network, drainage of coal mining, karst water exploitation