使用SSA-PNN优化阶段性降低方法对冷区域斜坡失效的多源数据驱动预测
1State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, China.
Scientific reports
|November 1, 2025
概括
这项研究引入了一个新的框架,将逐步减少方法 (SRM) 结合了Sparrow Search算法优化的概率神经网络 (SSA-PNN),以预测寒冷地区的斜率不稳定性. 该SSA-PNN模型准确评估斜坡退化,并改进地质危险的预警系统.
科学领域:
- 地质技术工程 地质技术工程
- 机器学习应用 机器学习应用
- 寒冷地区工程 寒冷地区工程
背景情况:
- 寒冷地区的山坡面临不稳定性,因为融周期会降解地质材料.
- 传统的方法很难捕捉这些斜坡的渐进性故障机制.
- 准确预测斜坡退化对于地理危险风险评估至关重要.
研究的目的:
- 开发一个综合框架来分析在融条件下的斜坡不稳定性.
- 改进空间时空斜坡退化机制的表征.
- 提高冷区地质危险的智能监测和预警系统的可靠性.
主要方法:
- 集成的逐步减少方法 (SRM) 与一只搜索算法优化的概率神经网络 (SSA-PNN).
- 利用多源数据集,包括现场监测,数值模拟和实验室测试.
- 通过使用安全系数 (FS) 作为标签的分类和回归任务来评估模型性能.
主要成果:
- 该SSA-PNN模型实现了高精度 (87.5%分类,R2=0.871回归).
- 性能优于XGBoost,SVM和物流回归等基准模型.
- 与传统PNN相比,在关键稳定性间隔中显著降低了错误分类率.
结论:
- SRM-SSA-PNN框架有效地模拟了结解引起的斜坡退化.
- 该框架提高了斜率不稳定机制的解释性.
- 为风险评估和在寒冷地区对地缘危险的早期预警提供了可靠的基础.
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