使用主要组件分析和支矢量机器进行地震滑坡易感性评估
Ziyao Xu1, Ailan Che2, Hanxu Zhou1
1School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
Scientific reports
|February 14, 2024
概括
这项研究开发了一种新模型,用于评估数据稀缺地区地震引发的山体滑坡的易感性. 该模型准确地确定了大高速公路沿线的高风险地区,改善了防灾准备.
科学领域:
- 地质科学 地质科学
- 地震工程的工程是地震工程.
- 自然危害评估自然危害评估
背景情况:
- 地震滑坡对基础设施和人类安全构成重大风险.
- 准确的山体滑坡易感性评估对于灾害管理至关重要,但往往受到有限的历史山体滑坡数据的阻碍.
- 地理和人为因素可能会掩盖过去滑坡活动的证据,使评估变得复杂.
研究的目的:
- 建立一个通用的地震滑坡易感性评估模型,适用于土地滑坡数据有限的地区.
- 将这个模型应用于海地区的戴高速公路,这是一个容易发生地震的地区.
- 为缺乏数据的地区提供一种新的地震滑坡易感性评估方法.
主要方法:
- 利用了来自类似地理区域的山体滑坡数据 (2014年卢迪安地震).
- 使用频率比方法来过微不足道的影响因素.
- 应用主要组件分析 (PCA) 用于减小维度和支持向量机 (SVM) 用于模型构建 (PCA-SVM).
主要成果:
- 该PCA-SVM模型实现了93.6%的高精度.
- 成海地区的山体滑坡易感性被分为五个级别,其中"非常高"易感性覆盖0.63%.
- 戴高速公路13公里的一段被确定为高风险区域,受到地震山体滑坡的严重影响.
结论:
- 开发的PCA-SVM模型提供了一种可靠的方法,用于在数据稀缺的环境中评估地震滑坡的易感性.
- 该研究成功地确定了大高速公路沿线的关键高风险地区,为目标灾害减缓战略提供了信息.
- 这项研究提出了一种可转移的方法,用于在类似的地理环境中评估地震山体滑坡危险.
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