Article: Deep learning-driven simulation modelling for mine geological risk assessment integrating multi-source data Journal: International Journal of Simulation and Process Modelling (IJSPM) 2026 Vol.23 No.5 pp.1 - 10 Abstract: Mining geological risk assessment is crucial for ensuring production safety, yet traditional methods relying on single data sources and expert experience suffer from low accuracy and delayed early warning, often failing to capture dynamic risk characteristics. This paper proposes a simulation-driven intelligent assessment framework that integrates multi-source monitoring data - including geological, subsidence, hydrological, and microseismic information-with advanced deep learning techniques. The framework is designed to dynamically simulate risk evolution processes and construct an end-to-end predictive model. Validation on public datasets demonstrates an accuracy of 91.3%, significantly surpassing the 78.5% achieved by traditional methods (<i>p</i> < 0.01), with high stability and generalisation ability. This process-modelling paradigm effectively overcomes the bottlenecks of information incompleteness and response delay, providing reliable technical support for geological hazard prevention and a foundational tool for intelligent mine construction, thereby supporting dynamic safety management. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-driven simulation modelling for mine geological risk assessment integrating multi-source data

Authors: Fuming Zhao; Chao Xie

Addresses: Anhui Institute of Geo-Environment Monitoring, Hefei 230001, China ' School of Artificial Intelligence and Computer Science, Hefei Normal University, Hefei 230001, China

Abstract: Mining geological risk assessment is crucial for ensuring production safety, yet traditional methods relying on single data sources and expert experience suffer from low accuracy and delayed early warning, often failing to capture dynamic risk characteristics. This paper proposes a simulation-driven intelligent assessment framework that integrates multi-source monitoring data - including geological, subsidence, hydrological, and microseismic information-with advanced deep learning techniques. The framework is designed to dynamically simulate risk evolution processes and construct an end-to-end predictive model. Validation on public datasets demonstrates an accuracy of 91.3%, significantly surpassing the 78.5% achieved by traditional methods (p < 0.01), with high stability and generalisation ability. This process-modelling paradigm effectively overcomes the bottlenecks of information incompleteness and response delay, providing reliable technical support for geological hazard prevention and a foundational tool for intelligent mine construction, thereby supporting dynamic safety management.

Keywords: simulation-driven modelling; multi-source data fusion; deep learning; mine geological risk assessment; risk evolution simulation; dynamic hazard prediction.

DOI: 10.1504/IJSPM.2026.152572

International Journal of Simulation and Process Modelling, 2026 Vol.23 No.5, pp.1 - 10

Received: 08 Dec 2025
Accepted: 18 Jan 2026
Published online: 27 Mar 2026 *

Title: Deep learning-driven simulation modelling for mine geological risk assessment integrating multi-source data

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