A novel approach for coal seam terrain prediction through information fusion of improved D–S evidence theory and neural network

• An integration approach for coal seam terrain prediction was proposed.
• Improved strategy was presented to reduce conflict and enhance fusion effect.
• The BPAs function was constructed through the weights extraction.
• Flowchart of the proposed approach was designed.
• Simulation example and comparison results were provided and discussed.

In order to effectively and accurately forecast the distribution of coal seam terrain, a novel prediction approach through information fusion of improved D–S evidence theory and neural network was proposed. An improved strategy based on confidence level was presented for evidence theory to reduce the conflicts between evidences and enhance the fusion effect. Moreover, BPAs function was constructed reasonably through extracting weights from preliminary prediction values of four neural networks, and the flowchart of proposed approach was designed. Furthermore, a simulation example was provided and some comparisons with other fusion prediction methods were carried out. The simulation example and comparison results indicated that the proposed approach was feasible, high-precision and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to specify the effect of proposed system.