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使用随机森林回传神经网络合模型对多种危险的敏感性评估:杭州市的案例研究
Bofan Yu1,2, Huaixue Xing3, Jiaxing Yan2
1China Geological Survey Nanjing Center, Nanjing, 210016, People's Republic of China.
Scientific reports
|September 18, 2024
概括
这项研究引入了一种新的随机森林反向传播神经网络模型,用于评估城市地质灾害风险,如地面崩塌和沉降. 这种先进的模型显著提高了预测准确性,为未来的全市风险评估提供了一个模板.
科学领域:
- 地质科学 地质科学
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 在大城市中,对区域地质灾害风险评估的需求不断增加.
- 传统基于知识的模型对敏感性评估的局限性.
- 数据驱动模型在地质危险评估中的潜力.
研究的目的:
- 为了评估杭州,一个大城市,地面崩塌和沉降的易感性.
- 提出和验证一个高精度的随机森林反向传播神经网络合模型.
- 为城市地质灾害管理制定多种危害易感性地图.
主要方法:
- 利用了基于现场调查和专家建议的九个评估因素.
- 采用随机森林反向传播神经网络合模型进行敏感性评估.
- 应用专家重量歧视和重叠评估方法用于多危险映射.
主要成果:
- 与传统模型相比,在模型性能指标 (AUC,精度,精度,回忆,F1得分) 中实现了3-40%的改进.
- 成功生成地质灾害的个人和多危险易感性地图.
- 确定并讨论了各种特征在预测过程中的重要性.
结论:
- 验证了用于评估城市地质灾害的先进机器学习模型的可行性.
- 证明了拟议的合模型在单个模型和传统方法上的优越性能.
- 为其他大城市的地质灾害风险评估提供了可复制的模板.
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