Jahanmiri Shirin, Noorian-Bidgoli Majid
Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.
Sci Rep. 2025 Jun 5;15(1):19841. doi: 10.1038/s41598-025-04109-x.
Underground coal mining presents significant environmental challenges, particularly land subsidence, which can lead to severe ecological and structural consequences. This phenomenon alters surface topography, disrupts groundwater flow, and poses risks to infrastructure, necessitating accurate predictive models for effective mitigation. Longwall mining, a widely used extraction method, is especially prone to inducing ground subsidence due to its caving process. This study leverages advanced machine learning techniques to enhance subsidence prediction accuracy and inform sustainable mining practices. Three hybrid models-biogeography-based optimization with gene expression programming (BBO-GEP), gray wolf optimizer with gene expression programming (GWO-GEP), and salp swarm algorithm with gene expression programming (SSA-GEP)-are applied to assess subsidence risks. Three hybrid models-BBO-GEP, GWO-GEP, and SSA-GEP-were developed and tested to enhance prediction accuracy and reduce model uncertainty. A comprehensive dataset comprising 11 key geotechnical and mining parameters, including seam thickness, mining depth, and various rock properties, was collected from 14 coal mines. Results indicate that the BBO-GEP model achieved the highest predictive accuracy, with a correlation coefficient of 0.99. Sensitivity analysis revealed that mining depth is the most influential factor in subsidence occurrence, whereas density has the least impact. These findings contribute to environmental risk management in mining by providing a robust predictive framework that aids in proactive subsidence mitigation strategies. The proposed models support decision-making for policymakers and industry stakeholders, fostering more sustainable mining operations with minimized ecological disruption.
地下煤矿开采带来了重大的环境挑战，尤其是地面沉降，这可能导致严重的生态和结构后果。这种现象改变了地表地形，扰乱了地下水流，并对基础设施构成风险，因此需要精确的预测模型来有效缓解。长壁开采是一种广泛使用的开采方法，由于其垮落过程，特别容易引发地面沉降。本研究利用先进的机器学习技术来提高沉降预测的准确性，并为可持续采矿实践提供依据。应用了三种混合模型——基于生物地理学优化的基因表达式编程（BBO-GEP）、基于灰狼优化器的基因表达式编程（GWO-GEP）和基于樽海鞘群算法的基因表达式编程（SSA-GEP）——来评估沉降风险。开发并测试了三种混合模型——BBO-GEP、GWO-GEP和SSA-GEP——以提高预测准确性并降低模型不确定性。从14个煤矿收集了一个包含11个关键岩土和采矿参数的综合数据集，包括煤层厚度、开采深度和各种岩石特性。结果表明，BBO-GEP模型实现了最高的预测准确性，相关系数为0.99。敏感性分析表明，开采深度是沉降发生的最有影响因素，而密度的影响最小。这些发现通过提供一个强大的预测框架，有助于制定主动的沉降缓解策略，从而为采矿中的环境风险管理做出贡献。所提出的模型支持政策制定者和行业利益相关者的决策，促进更可持续的采矿作业，同时将生态破坏降至最低。
