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Updated: Jun 15, 2025

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使用斜率稳定性故障机器学习预测与使用不同算法训练的元启发技术进行碎片流易感性的数值模型
Kennedy C Onyelowe1,2,3, Arif Ali Baig Moghal4, Furquan Ahmad5
1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria. konyelowe@mouau.edu.ng.
Scientific reports
|August 22, 2024
概括
智能数值模型使用机器学习和元启发方法预测碎片流的易感性. 适应性神经模糊推断系统达到超过85%的准确性,提供了具有成本效益的斜率稳定性分析.
科学领域:
- 地质技术工程 地质技术工程
- 计算科学 计算科学
- 自然危害评估自然危害评估
背景情况:
- 斜坡稳定性分析对于碎片流危险评估至关重要.
- 传统的现场研究用于垃圾流量监测是昂贵和耗时的.
- 开发智能模型可以降低成本,提高斜率行为分析的效率.
研究的目的:
- 开发智能数值模型来预测碎片流的易感性.
- 通过使用新的元启发式训练方法,提高机器学习模型的性能.
- 为斜坡设计和监测提供一种具有成本效益和时间效率的方法.
主要方法:
- 开发智能数值模型用于安全因素 (FOS) 预测.
- 应用新的元启发方法来训练机器学习模型.
- 使用自适应的神经模糊推理系统 (ANFIS) 结合粒子群优化 (PSO).
主要成果:
- 该ANFIS-PSO模型在预测碎片流量FOS.中表现出卓越的性能.
- 在FOS预测中获得了超过85%的准确性,超过了其他测试方法.
- 使用多个性能评估指数验证模型准确性.
结论:
- 智能模型为碎片流易感性预测提供了强大而高效的替代方案.
- 超启发式训练显著提高了机器学习模型的性能.
- 开发的ANFIS-PSO模型为成本效益高的斜坡稳定性评估提供了可靠的工具.
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