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一个基于知识的深度学习模型用于对香港的山体滑坡易感性评估
Li Chen1, Peifeng Ma2, Xuanmei Fan3
1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China; Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong.
The Science of the total environment
|May 28, 2024
概括
这项研究通过将滑坡先例与深度学习模型相结合,提高了滑坡预测的准确性和稳定性. 这种新的方法通过结合物理约束和知识意识技术,优于现有方法.
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:
- 数据驱动的滑坡易感性预测模型在很大程度上取决于数据质量,模型结构和参数调整.
- 现有的几种方法包含了山体滑坡的先验 (山体滑坡发生的知识或统计数据),以提高对山体滑坡机制的理解.
- 在滑坡预测中提高模型的可转移性和稳定性仍然是一个挑战.
研究的目的:
- 将滑坡先验与深度学习模型结合起来,以提高预测的可转移性和稳定性.
- 开发一种基于知识的方法,用于绘制山体滑坡易感性的地图.
- 通过模型解释识别关键的山体滑坡因果因素.
主要方法:
- 根据山体滑坡统计数据选择的非山体滑坡样本.
- 使用变化自动编码器解开纠的山体滑坡特征.
- 制作了一个损失函数与物理约束.
- 使用SHAP方法来解释深度学习模型.
- 集成的MT-InSAR数据用于土地滑坡易感性地图增强和交叉验证.
主要成果:
- 综合模型在准确性,精度,回忆,F1得分,AuROC和Cohen Kappa方面表现优于其他数据驱动方法.
- 倾斜被确定为发生山体滑坡的最有影响的因素.
- 结合极端降雨先验,改善了降雨的特征排名,特别是在像香港这样的地区.
- MT-InSAR数据增强提高了交叉验证的效率.
结论:
- 基于知识的深度学习方法显著增强了山体滑坡易感性预测模型.
- 这种方法改善了模型的概括性,并减轻了来自不平衡数据集的训练偏差.
- 整合滑坡先验为更强大,更可靠的滑坡预测系统提供了一个有希望的方向.
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