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关于机器学习预测和预警模型的研究,用于云南省降雨引起的山体滑坡
Jia Kang1, Bingcheng Wan2, Zhiqiu Gao1
1School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Scientific reports
|June 18, 2024
概括
这项研究开发了云南省的山体滑坡易感性地图 (LSM) 和降雨引起的山体滑坡 (RIL) 预测模型. 随机森林 (RF) -LSM模型证明了对滑坡风险评估的卓越预测准确性.
科学领域:
- 地质科学 地质科学
- 地质危险评估项目
- 机器学习应用 机器学习应用
背景情况:
- 滑坡意味着重大地质灾害,威胁生命和财产.
- 云南省容易发生山体滑坡,需要有效的风险管理策略.
- 准确的山体滑坡预测对于减轻灾害和公共安全至关重要.
研究的目的:
- 分析导致山体滑坡的因素,并为云南省创建山体滑坡易感性地图 (LSM).
- 开发和比较机器学习 (ML) 模型,用于预测区域降雨引起的山体滑坡 (RIL).
- 确定一个有效的RIL预测模型的最佳ML算法和输入因子.
主要方法:
- 利用历史的山体滑坡数据和八个基本因素 (海拔,斜率,面积,石质,土地覆盖面,NDVI,土壤类型,AAP).
- 采用频率比 (FR) 方法来评估因子灵敏度并生成一个LSM.
- 对比了五种ML算法 (XGBoost,KNN,SVM,LR,RF) 用于RIL概率预测,其中包括降雨数据.
主要成果:
- 土地滑坡易感性地图 (LSM) 有效地确定了容易发生山体滑坡的地区.
- 随机森林 (RF) 模型与LSM (RF-LSM) 结合,在预测RIL.4.0方面取得了最高的准确性 (ACC=0.906,AUC=0.954).
- RF-LSM模型表现出强的性能,测试组的检测概率 (POD) 为0.96,验证组的检测概率为0.8.
结论:
- 推RF-LSM模型作为云南省的主要RIL预测模型.
- 该研究强调了ML在提高山体滑坡预测和风险评估方面的有效性.
- 建立基于RF-LSM模型的预警级别可以改善区域灾害准备.
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