使用可解释的CNN深度学习模型,预测中国土地对沉降危险的易感性
Kai Liu1, Jianxin Zhang2, Junfei Liu2
1School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China; Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University at Zhuhai, Zhuhai 519087, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, China.
The Science of the total environment
|December 25, 2023
概括
中国的土地沉降易感性受到人口,GDP和地下水变化的重大影响. 未来的预测表明,高风险地区将扩大,使人口和基础设施暴露于这种地缘环境危害中.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 地理危险 地区性危险
背景情况:
- 土地沉降是一个关键的全球地缘环境危险,需要准确的易感性评估.
- 了解土地沉降危险易感性 (LSHS) 的驱动因素和空间分布对于预防和减轻灾害至关重要.
研究的目的:
- 开发和验证一个可解释的模型,用于评估中国的土地沉降危险易感性 (LSHS).
- 识别和排名影响LSHS的关键自然和人为因素.
- 预测未来的LSHS和相关的人口和基础设施暴露.
主要方法:
- 使用可解释卷积神经网络 (CNN) 进行LSHS建模.
- 采用了SHapley添加式解释 (SHAP) 来解释CNN模型的特征贡献.
- 分析了历史 (2004-2016) 和预测的未来 (21世纪中后期) LSHS.
主要成果:
- 美国有线电视新闻 (CNN) 模型表现出高精度 (AUC=0.9939,F1分数=0.9566).
- 人口,GDP和地下水储存变化被确定为中国LSHS的主要驱动因素.
- 预计高和非常高的LSHS地区将大幅扩大,对人口和基础设施的暴露将增加.
结论:
- 该研究成功地为中国建立了一个可解释的LSHS模型,突出了关键影响因素.
- 这些发现强调了土地沉降的威胁日益加剧,需要采取积极的适应战略.
- 这项研究提供了重要的数据,以告知区域政策,以减轻沉降造成的损害.
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