"合并转移学习方法用于减少数据恐惧区域的山体滑坡易感性绘图中的错误"
Ankit Singh1, Nitesh Dhiman1, K C Niraj2
1School of Civil and Environmental Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India.
Scientific reports
|November 23, 2024
概括
转移学习 (TL) 改善了希马拉雅地区的滑坡易感性地图 (LSMs). 这种方法有效地从数据丰富的领域转移知识,以提高数据收集具有挑战性的山体滑坡预测准确性.
科学领域:
- 地质科学 地质科学
- 机器学习应用 机器学习应用
- 遥感 遥感 遥感 遥感
背景情况:
- 滑坡易感性地图 (LSMs) 对危险评估至关重要,但在喜马拉雅山脉等地区收集数据是昂贵和困难的.
- 现有的LSM方法面临的局限性是由于数据稀缺性在山地地形.
研究的目的:
- 提出和评估一种转移学习 (TL) 方法,以提高LSM在数据稀缺地区的表现.
- 将知识从数据丰富的来源区域转移到数据贫困的目标区域,以改进滑坡预测.
主要方法:
- 利用了两种机器学习模型,即随机森林 (RF) 和多层感知器 (MLP),训练了滑坡导电因素.
- 通过在源区 (Mandi) 的培训模型应用转移学习,并将其应用于目标区 (Kullu,Rudraprayag).
- 通过KL分歧评估地形相似性,并通过多对线性测试分析因子独立性. 使用AUC-ROC,精度,回忆,F-score和准确度来评估性能.
主要成果:
- TL方法显著改善了目标区域的LSM性能,增加了RF和MLP模型的AUC值.
- 对于目标区域1,RF AUC从0.908增加到0.942 (TL) 和0.959 (组合);MLP AUC从0.896增加到0.907 (TL) 和0.946 (组合).
- 目标区域2显示RF AUC从0.95 (自训练) 提高到0.98 (组合),MLP AUC从0.82增加到0.84 (组合).
结论:
- 拟议的转移学习方法有效地提高了LSM在数据稀缺的喜马拉雅地区的表现.
- TL提供了一种有前途的解决方案,以克服滑坡易感性测绘中的数据限制.
- 这种方法可以帮助更准确,更具成本效益地评估山体滑坡危险.
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