通过基于梯度增强的智能混合模型对采矿土地沉降的研究,具有分类特征支持算法
Biao Zhang1, Chun Xu1, Xingguo Dai1
1School of Resources and Safety Engineering, Central South University, Changsha, 410083, Hunan, China.
Journal of environmental management
|February 20, 2024
概括
这项研究引入了混合机器学习模型来预测煤矿引发的土地沉降 (MLS). 与CatBoost (SSA-CatBoost) 结合的Sparrow搜索算法显著提高了预测准确性,为环境和安全管理提供了一种可靠的方法.
科学领域:
- 地质科学和环境科学 地球科学和环境科学
- 人工智能和机器学习
背景情况:
- 煤矿引发的土地沉降 (MLS) 对基础设施和生态系统构成重大风险.
- 准确预测MLS是复杂的,并且严重依赖于研究人员的专业知识.
研究的目的:
- 开发和评估用于预测MLS的混合机器学习模型.
- 为了比较与CatBoost模型集成的五个智能优化算法的性能.
主要方法:
- 开发了五种混合型号:狮优化器-CatBoost (ALO-CatBoost),白搜索-CatBoost (BES-CatBoost),鸟算法-CatBoost (BSA-CatBoost),哈里斯霍克斯优化-CatBoost (HHO-CatBoost) 和雀鸟搜索算法-CatBoost (SSA-CatBoost) 等.
- 使用R2,RMSE,MAE和VAF等指标对预测准确度和可靠性的比较分析.
- 使用Shapley方法分析特征的重要性和对预测的贡献.
主要成果:
- 所有混合模型都显示出比单个模型更好的预测性能.
- SSA-CatBoost模型取得了最显著的改进,R2从0.927增加到0.965.
- 功能重要性分析提供了对影响MLS预测因素的见解.
结论:
- 混合模型技术为预测煤炭开采造成的土地沉降提供了一种可靠的方法.
- 这项研究为采矿技术人员提供了有价值的工具,以评估MLS的影响,并为安全和环境管理策略提供信息.
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