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建筑对山体滑坡的脆弱性:中国新兴县的广泛评估

Fengting Shi¹, Ling Li², Xueling Wu¹

¹School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|July 13, 2024

概括

这项研究开发了一种机器学习模型,用于评估新兴县建筑物对山体滑坡的脆弱性. 该模型准确地识别了高风险地区,使得有针对性的缓解努力和改善社区安全.

科学领域:

地质科学和环境科学地球科学和环境科学
人工智能和机器学习
土木工程和城市规划.

背景情况:

滑坡对建筑物和基础设施构成重大风险,需要准确的脆弱性评估.
现有的土地滑坡脆弱性评估方法往往缺乏有效风险管理所需的精度.
开发强大的模型对于保护容易发生山体滑坡的地区的社区至关重要.

研究的目的:

使用机器学习开发和验证一个综合模型来评估建筑物对山体滑坡的脆弱性.
量化山体滑坡危险风险和建筑物耐力,以进行全面的脆弱性评估.
在新兴县确定特定的村级需求,并优先考虑缓解战略.

主要方法:

采用卷积神经网络 (CNN) 模型,根据九个因果因素和历史滑坡数据,推导出滑坡危险风险.
建筑的阻力被量化推导,并与危险风险集成,以评估建筑的脆弱性.
村级的易感性是使用分村统计数据计算出来的,其次是对山体滑坡危险类别的分类.

主要成果:

建筑易感性与山体滑坡危险有正相关性,与建筑阻力有负相关性.
该模型成功地确定了新兴县各地不同程度的建筑易感性.
模型预测的现场验证实现了94.5%的高准确率.

结论:

开发的综合模型提供了一种可靠和准确的方法来评估建筑物对山体滑坡的脆弱性.
这些发现强调了在脆弱性评估中考虑危险风险和构建抵抗力的重要性.
这项研究为土地利用规划和在易发生山体滑坡的地区减少灾害风险提供了有价值的工具.

关键词:

在美国,CNN是CNN.建立抵抗力的抵抗力.发生山体滑坡的危险定量风险建模定量风险建模

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