优化的内核极端学习机器使用正弦弦算法来预测MICP水泥土的不受限制的压缩强度
Shuquan Peng1, Qiangzhi Sun1, Ling Fan2
1School of Resources and Safety Engineering, Central South University, Changsha, Hunan, 410083, People's Republic of China.
概括
这项研究引入了一种机器学习方法,使用正弦共弦算法 (SCA) 来优化内核极端学习机器 (KELM) 来预测微生物诱导的石沉 (MICP) 凝土土的未受限制的压力强度 (UCS),显示出更好的准确性.
科学领域:
- 地质技术工程 地质技术工程
- 环境科学 环境科学
- 计算智能是一种计算智能.
背景情况:
- 微生物诱导的石沉 (MICP) 是一种可持续的土壤整治技术.
- 准确预测不受限制的压力强度 (UCS) 对于评估MICP处理土壤的有效性至关重要.
- 现有的机器学习模型需要优化,以便精确的UCS预测.
研究的目的:
- 开发和评估一种新的机器学习模型,用于预测MICP水泥土的UCS.
- 为了优化内核极端学习机器 (KELM) 使用正弦代数算法 (SCA).
- 将拟议的SCA-KELM模型的性能与已建立的算法比较,例如随机森林 (RF) 和支持矢量机器 (SVM).
主要方法:
- 使用了180个UCS测量的MICP水泥土的数据集.
- 使用Sine Cosine算法 (SCA) 来优化KELM的调整系数 (C) 和内核宽度 (γ).
- 使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和R平方 (R2) 度量来评估性能.
主要成果:
- 该SCA-KELM模型表现出优异的预测性能,总得分为21.
- 使用SCA的优化显著提高了KELM的业绩110%.
- 一种创新的方法根据重要性得分将影响因素分为高,中,低敏感度等级.
结论:
- SCA-KELM算法是一种强大而准确的工具,用于预测MICP水泥土的UCS.
- SCA优化大大提高了KELM在地质技术应用中的预测能力.
- 因子灵敏度分类为MICP土壤行为提供了宝贵的见解.
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