数字模拟和机器学习在废物流危险映射中的联合应用
Ruiyuan Gao1, Ang Wang2, Hailiang Liu3
1Civil Engineering and Construction Center, Huanghe Science and Technology University, Zhengzhou, 450063, China. gry@hhstu.edu.cn.
Scientific reports
|August 21, 2025
概括
这项研究引入了使用机器学习与数值模拟相结合的更快的碎片流危险映射 (DFHM) 方法. 这种新方法显著加快了危险评估,为大规模绘图提供了更有效的解决方案.
科学领域:
- 地质科学
- 计算科学
- 环境工程
背景情况:
- 废物流危险映射 (DFHM) 对于减轻废物流风险至关重要.
- 传统的DFHM依赖于耗时的数值模拟来确定碎片流动强度.
研究的目的:
- 开发和评估数字模拟和机器学习的综合框架,以实现高效的DFHM.
- 评估机器学习模型的速度和准确性,作为DFHM中传统液压模拟的替代方案.
主要方法:
- 使用FLO-2D模型模拟20年,50年和100年的碎片流.
- 收集的碎片流深度和速度作为依赖变量,以及地形和土地覆盖数据作为独立变量.
- 使用20年和50年数据集训练了一种渐变增强决策树 (GBDT) 机器学习模型,并用100年数据集验证它.
主要成果:
- 机器学习预测与数值模拟结果密切相匹配, 验证了提出的框架.
- 与传统的数值模拟相比,机器学习方法的速度提高了十倍以上.
- 该研究证实了整合机器学习以提高DFHM效率的有效性和合理性.
结论:
- 机器学习模型显示出作为大型DFHM液压模拟的有效替代品的巨大潜力.
- 拟议的综合框架将大大提高碎片流危险评估的效率.
- 这项研究为更快,更可扩展的垃圾流风险管理策略铺平了道路.
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