摘要
为了解决排土场滑坡造成的事故多发、伤亡严重的问题,提出了一种基于mRBF-ELM集成模型的排土场滑坡预警方法,将排土场预警指标数据输入到经过训练完成的多径向基函数极限学习机集成模型中,结合所有子模型的稳定系数输出,集成模型的输出结果为综合所有子模型预测结果后的输出。作为对比,将应用mRBF-ELM集成模型得到的预测结果与应用人工神经网络(ANN)、极限学习机(ELM)、高斯径向基函数极限学习机(RBF-ELM^(1))、双曲正切径向基函数极限学习机(RBF-ELM^(2))、逆多二次径向基函数极限学习机(RBF-ELM^(3))的预测结果进行对比分析,从平均相对误差、均方根误差、拟合优度3个方面验证模型的可靠性。结果表明,该预警模型评估非常可靠,有很好的推广应用价值。
Dump yard landslide accidents occur frequently,with serious casualties and severe social impact.The scientific landslide early warning method of dump yard is of great significance for accident prevention and risk management.This paper proposes a dump landslide based on the mRBF-ELM integrated model.Early warning method,input the early warning index data of the dumping site into the trained multi-radial basis function extreme learning machine integrated model,combine the output of the stability coefficients of all sub-models,and the output result of the integrated model is after the prediction results of all sub-models are integrated output.As a comparison,the prediction results obtained by applying the mRBF-ELM integrated model are compared with the application of artificial neural network(ANN),extreme learning machine(ELM),Gaussian radial basis function extreme learning machine(RBF-ELM^(1)),hyperbolic tangent radial basis.The prediction results of the function extreme learning machine(RBF-ELM^(2))and the inverse multiple quadratic radial basis function extreme learning machine(RBF-ELM^(3))are compared and analyzed from the three aspects of average relative error,root mean square error,and goodness of fit to verify the reliability of the model.The results show that the early warning model has high evaluation reliability and good value in popularization and application.
作者
邓明月
栾婷婷
李运
佟雪奇
DENG Mingyue;LUAN Tingting;LI Yun;TONG Xueqi(Beijing Institute of Petrochemical Technology,Beijing 102617,China)
出处
《北京石油化工学院学报》
2022年第4期47-52,共6页
Journal of Beijing Institute of Petrochemical Technology
作者简介
邓明月(1992-),女,硕士研究生,研究方向为安全工程,E-mail:1004798571@qq.com。
