评估多种危害对冰层危害的敏感性:从阿拉斯加的例子中吸取教训
Letizia Elia1, Silvia Castellaro1, Ashok Dahal2
1Department of Physics and Astronomy, Alma Mater Studiorum University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy.
The Science of the total environment
|July 8, 2023
概括
科学家们开发了数据驱动的模型,以预测永久土的融化危险,如阿拉斯加的逆向融化 (RTSs) 和活跃层脱离 (ALDs). 这有助于理解北极地区变暖的景观变化.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 气候变化研究 气候变化研究
背景情况:
- 地形学过程的易感性在中低度是标准的,但在危冰地区未得到充分探索.
- 全球变暖正在加速危冰性环境的变化,需要了解地质形态动态.
- 永久土的退化引发了冰层危险,如逆行解 (RTS) 和活性层脱离 (ALD),影响基础设施并释放温室气体.
研究的目的:
- 探索数据驱动的模型,以确定在危险的环境中容易出现RTS和ALD的位置.
- 用二项式通用添加模型估计北阿拉斯加地区RTS和ALD发生的概率.
- 开发一个自动化的,开源的Python工具来复制空间预测实验.
主要方法:
- 利用双项通用添加模型结构来分类景观易感性.
- 测试模型准确性使用适合性 (AUC),随机交叉验证和空间交叉验证程序.
- 开发了一个基于Python的自动化分析协议,用于数据预处理和空间预测.
主要成果:
- 二进制分类器在识别易患RTS (AUC=0.83) 和ALD (AUC=0.86) 的位置方面表现出很高的准确性.
- 交叉验证结果证实了模型的稳定性,随机交叉验证的平均AUC值为0.82 (RTS) 和0.86 (ALD),空间交叉验证为0.74 (RTS) 和0.80 (ALD).
- 创建了一个开源的Python工具来自动化整个分析协议,从而实现复制和应用.
结论:
- 数据驱动的模型,特别是通用添加模型,对于预测危险地区的冰层危险是有效的.
- 开发的分析协议和开源工具促进了RTS和ALD的自动空间预测.
- 了解这些地形学动态对于快速变化的北极环境中的决策和预测低度的未来变化至关重要.
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