基于ISSA-RF模型的多个特征参数考虑软地面的定居预测研究
Changshuai Sun1, Tianwen Yu1, Min Li2,3
1Shandong Electric Power Engineering Consulting Institute Corp., Ltd, Jinan, 250013, China.
Scientific reports
|March 1, 2024
概括
这项研究引入了一种改进的搜索算法 (ISSA),以优化随机森林 (RF) 模型,以准确地预测软基础定居点. ISSA-RF模型在预装工程项目中显著提高了预测准确性.
科学领域:
- 地质技术工程 地质技术工程
- 机器学习 机器学习
- 计算智能是一种计算智能.
背景情况:
- 准确的结算预测对于在软基础上预装工程项目的成功至关重要.
- 现有的定居点预测模型往往在准确性和效率方面面临挑战.
研究的目的:
- 为软基础开发一个高度准确的结算预测模型.
- 使用改进的优化算法来提高随机森林 (RF) 模型的性能.
主要方法:
- 使用预装工程项目的数据创建了一个定居点预测数据库.
- 开发了一种改进的乌搜索算法 (ISSA),结合了混乱映射,自适应权重和Levy飞行.
- 使用ISSA优化随机森林 (RF) 模型的超参数,创建ISSA-RF模型.
主要成果:
- 与其他优化算法相比,ISSA在基准函数上的精度和稳定性更高.
- 在实际应用中,ISSA-RF模型显示出与标准射频模型相比,预测准确度和适用性显著提高.
结论:
- ISSA-RF模型为软基础结算预测提供了强大而准确的解决方案.
- 这种方法为有效规划和执行预装载工程项目提供了宝贵的指导.
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