增强基于深度学习的斜率稳定性分类,使用新的元启发式优化算法来进行特征选择
Bilel Zerouali1, Nadjem Bailek2,3, Aqil Tariq4
1Laboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, B.P. 78C, 02180, Ouled Fares, Chlef, Algeria.
Scientific reports
|September 18, 2024
概括
机器学习模型,特别是生成对抗网络 (GAN),有效地对斜率稳定性进行分类. 将特征选择与GAN结合起来,可显著提高关键地质工程应用的准确性.
科学领域:
- 地质技术工程 地质技术工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 斜坡稳定性对于基础设施安全和危险减轻至关重要.
- 确定影响斜坡稳定的关键因素对于准确的评估至关重要.
- 传统方法可能无法完全捕捉斜率稳定性预测的复杂性.
研究的目的:
- 确定影响斜坡稳定的最有影响的因素.
- 为了评估各种机器学习模型的性能,用于斜坡稳定性分类.
- 评估高级特征选择技术对模型性能的影响.
主要方法:
- 相关性分析和随机森林回归因特征的重要性.
- 深度学习模型的评估:RNN,LSTM和GAN.
- 将二进制bGGO特征选择与GAN模型集成.
主要成果:
- 凝聚力,单位重量,斜坡高度和摩擦角度被确定为关键因素.
- 该GAN模型实现了高精度 (0.913) 和AUC (0.9285).
- bGGO-GAN模型表现出卓越的性能,在627个样本上达到95%的准确性.
结论:
- 先进的机器学习,特别是具有特征选择的GAN,显著改善了坡度稳定性分类.
- bGGO-GAN模型提供了高精度,可概括性和增强的预测值.
- 这种方法为地质工程和危险减轻提供了宝贵的见解.
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