Coal Mine Safety Alert System: Refining BP Neural Network with Genetic Algorithm Optimization

TL;DR

This paper introduces a GA-optimized BP neural network for coal mine safety alerts, demonstrating improved risk detection and timely intervention capabilities in a real-world Chinese coal mine.

Contribution

It develops a novel hybrid GA-BP neural network model specifically for coal mine safety early warning systems, enhancing traditional neural network performance.

Findings

GA-BP outperforms traditional BP neural networks

Model effectively identifies potential safety risks

Enables timely safety interventions

Abstract

In response to the persistent safety challenges within coal mines, this study proposes a novel approach integrating a three-layer feedforward backpropagation artificial neural network with a genetic algorithm (GA-BP) for establishing a safety early warning system. Focused on a coal mine in Shandong, China, the model's effectiveness is evaluated using relevant data for training and analysis. Results indicate the superiority of the GA-BP model over traditional BP neural networks, offering enhanced capability for identifying potential safety risks promptly. This advancement enables coal mine management to implement timely interventions, ensuring the safety of miners. The findings present valuable insights for engineering applications in similar contexts.