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Shahab Hosseini¹, Jitendra Khatti², Blessing Olamide Taiwo³

¹Faculty of Engineering, Tarbiat Modares University, Tehran, Iran.

Scientific reports

|October 31, 2023

概括

一个新的黑洞优化的长短期记忆 (LSTM) 模型有效地预测了采矿爆炸中的地面振动. 这种先进的混合型号的性能优于传统方法,为关键的安全评估提供了高精度.

科学领域:

采矿工程采矿工程采矿工程
计算科学计算科学
地质物理学地质物理学

背景情况:

爆炸造成的地面振动在采矿操作中构成重大风险.
准确预测这些振动对于安全和环境管理至关重要.
现有的计算模型在精确评估爆炸引起的地面振动方面存在局限性.

研究的目的:

引入一个最佳的计算模型来评估在采矿爆炸期间的地面振动.
为了比较传统,先进的机器学习,深度学习和混合模型的预测性能.
为了评估一个新的黑洞优化长短期记忆 (LSTM) 模型的有效性.

主要方法:

采用了162个数据点来训练和测试各种模型,包括LSTM,人工神经网络 (ANN),最小方位支持向量机 (LSSVM),集合树 (ET),决策树 (DT),高斯过程回归 (GPR),支持向量机 (SVM) 和多线性回归 (MLR).
首次引入并使用黑洞优化的LSTM模型 (PPV11) 来预测地面振动.
应用了十五个性能指标,外部交叉验证,文献验证,ANOVA,Z测试和安德森-达林测试,用于全面的模型评估.

主要成果:

黑洞优化的LSTM模型 (PPV11) 在预测地面振动方面表现出卓越的性能,实现RMSE = 0.0181 mm/s,MAE = 0.0067 mm/s,R = 0.9951,a20 = 96.88,IOA = 0.9719,IOS = 0.0356.
混合模型,特别是优化的LSTM,对多对线性敏感性降低.
统计测试证实了PPV11模型预测的可靠性和准确性,而GPR和LSSVM表现出过问题.

结论:

黑洞优化的LSTM模型 (PPV11) 在矿山项目中在爆炸过程中预测地面振动方面具有很高的能力和准确性.
与优化算法集成的混合模型提供了增强的预测准确性和针对多对应线性的稳定性.
该研究证实了先进的人工智能技术在通过精确的地面振动评估来提高采矿安全性和运营效率方面的巨大潜力.

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