基于卷积神经网络的深度学习用于巴赫特根流域的山体滑坡易感性测绘
Li Feng1, Maosheng Zhang1, Yimin Mao2
1School of Human Settlements and Civil Engineering, Xi'AnJiaotong University, Xi'An, 710049, China.
Scientific reports
|April 17, 2025
概括
使用卷积神经网络 (CNN) 的深度学习显著提高了山体滑坡易感性绘图的准确性. 这种先进的方法可以识别高风险区域,这对于有效地减轻山体滑坡危险和降低风险的战略至关重要.
科学领域:
- 地质科学 地质科学
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 滑坡对安全和基础设施构成重大风险.
- 传统的易感性模型在复杂的空间数据上扎.
- 准确的山体滑坡评估对于风险管理至关重要.
研究的目的:
- 使用深度学习开发高精度的山体滑坡易感性地图.
- 评估卷积神经网络 (CNN) 在滑坡预测中的有效性.
- 确定巴赫塔根流域的高风险区域,以进行有针对性的缓解.
主要方法:
- 创建了235个地点的全面的山体滑坡清单.
- 使用了15个条件因素 (地形,地质,水文,气候).
- 一个卷积神经网络 (CNN) 模型被训练并验证.
主要成果:
- 美国有线电视新闻网的模型达到95.76%的准确率和95.11%的精度.
- 低误差指标 (MAE,MSE,RMSE) 证实了模型的可靠性.
- 北部和东北部巴赫塔根流域被确定为高度易受影响.
结论:
- 深度学习 (CNNs) 提供了一个强大的,可扩展的解决方案,用于土地滑坡易感性映射.
- 该研究为城市规划者和政策制定者提供了关键数据.
- 在确定的高风险地区,积极的缓解策略是必不可少的.
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