在西喜马拉雅山脉使用机器学习和数值建模进行雪崩易感性,危险性和暴露性评估
1School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India.
Scientific reports
|October 31, 2025
概括
雪崩威胁西喜马拉雅地区的社区. 机器学习模型确定了高风险区域,显示该地区约8%是高度敏感的,这对于雪崩危险缓解和预测至关重要.
科学领域:
- 地质科学和环境科学 地球科学和环境科学
- 在自然危害评估中的计算建模和机器学习应用.
背景情况:
- 雪崩是一个重要的自然危险,每年经常影响西喜马拉雅地区的基础设施和社区.
- 现有的雪崩评估方法往往缺乏在复杂地形中有效的风险管理所需的空间分辨率和预测准确性.
研究的目的:
- 评估和比较各种机器学习算法的有效性,以评估盆地规模的雪崩易感性.
- 在西喜马拉雅山脉的Candra-Bhaga和Upper Beas盆地内建模和量化雪崩危险和暴露.
- 为特定地点的雪崩预测和识别高风险区域 (热点) 提供关键数据.
主要方法:
- 使用了一套机器学习算法,包括随机森林,支持向量机,物流回归和人工神经网络.
- 使用全面的雪崩预测因素数据集对模型性能进行比较.
- 采用数值建模来模拟雪崩场景,并分析不同释放深度 (0.5米和3米) 的危险和暴露情况.
主要成果:
- 随机森林模型表现出优异的性能,准确度为88.73%,敏感性评估的曲线下面面积 (AUC-ROC) 为0.95.
- 大约8%的研究区域,特别是拉哈乌尔和斯皮蒂地区,对雪崩的敏感性很高.
- 模拟显示约161建筑和7个湖泊暴露在雪崩中,释放深度为0.5米,在释放深度为3米的场景下增加到约557建筑和9个湖泊.
结论:
- 机器学习,特别是随机森林算法,为西喜马拉雅地区的大规模雪崩易感性测绘提供了强大的框架.
- 该研究确定了易受雪崩影响的关键地区和基础设施,强调了有针对性的缓解战略的必要性.
- 调查结果提供了必要的基线数据,以加强该地区的当地雪崩预测和风险管理计划.
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