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使用随机森林方法评估台湾中部的液化潜力

Chih-Yu Liu¹, Cheng-Yu Ku², Yu-Jia Chiu¹

¹Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, 202301, Taiwan.

Scientific reports

|November 11, 2024

概括

本研究使用随机森林 (RF) 方法准确预测台湾的土壤液化潜力. 先进的射频模型实现了98.89%的准确性,超过了传统的地震危险评估方法.

科学领域:

地质技术工程地质技术工程
地震学地震学
机器学习应用机器学习应用

背景情况:

土壤液化在地震活跃地区构成重大地质技术危险,影响基础设施和公共安全.
准确预测液化潜力对于地震风险评估和减缓策略至关重要.

研究的目的:

用随机森林 (RF) 机器学习方法评估台湾中部的液化潜力.
将射频模型的性能与液化预测的传统简化程序进行比较.

主要方法:

使用540个土壤和地震参数的数据集开发射频模型.
包括诸如深度,应力,SPT-N值,细度含量,地震强度和地面加速峰值等因素.
使用交叉验证和与历史液化数据的比较进行严格的验证.

主要成果:

射频模型实现了98.89%的高预测准确度.
SPT-N值被确定为最关键的土壤因素,而地面加速峰值作为关键的地震因素.
与简化程序相比,RF模型表现出更高的性能,即使输入变量更少.

结论:

随机森林方法在预测土壤液化潜力方面非常有效.
开发的射频模型为地震危险评估提供了比传统方法更准确和更强大的方法.
这项研究为地震易发地区的地质工程师和城市规划人员提供了有价值的工具.

关键词:

人工神经网络的人工神经网络地震地震地震地震地震液化液化是一种液化过程.这是一个潜在的潜力.随机的森林随机的森林地震活动的地震活动.

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