在东南亚地区使用当代深度学习模型和夏普利添加式扩展 (SHAP) 技术,探索月度降水中的稳定同位素模式
Mojtaba Heydarizad1, Nathsuda Pumijumnong2, Masoud Minaei3,4
1State Key Laboratory of Marine Geology, Tongji University, Shanghai, People's Republic of China.
Isotopes in environmental and health studies
|June 16, 2025
概括
机器学习,特别是深度神经网络 (DNN),可以准确地模拟东南亚的稳定同位素沉. 这种方法克服了该地区的数据限制,推动了气候和水循环研究.
科学领域:
- 水文学和气候学
- 机器学习应用 机器学习应用
背景情况:
- 稳定的同位素对于了解热带水循环和气候动态至关重要.
- 东南亚在收集降水同位素数据方面面临挑战,原因是高成本和物流.
- 有限的同位素数据阻碍了该地区全面的气候分析.
研究的目的:
- 开发和评估用于模拟热带降雨中稳定同位素含量的机器学习模型.
- 评估气候远程连接指数和当地气象参数对降水同位素的影响.
- 将深度学习模型的性能与传统的统计方法进行比较.
主要方法:
- 利用深度神经网络 (DNN) 模型进行稳定同位素模拟.
- 将DNN性能与部分最小平方回归 (PLSR) 模型进行比较.
- 使用SHAP (夏普利添加式解释) 来确定特征的重要性和模型可解释性.
主要成果:
- DNN模型在模拟六个东南亚站的稳定同位素含量方面表现出卓越的准确性.
- 特性重要性分析证实了降水和潜在蒸发的重大影响.
- SHAP分析揭示了关键特征和预测的同位素值之间的非线性关系.
结论:
- 深度学习模型对于模拟热带降雨中的稳定同位素是有效的,解决了数据稀缺问题.
- 该研究提供了一个框架,用于将机器学习应用于数据有限的热带地区的同位素水文学.
- 这些发现提高了对影响热带水循环的气候驱动因素的理解.
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