一个集成的机器学习框架,用于预测人为和天然同位素在南中国海的不确定性量化量化
1University of Chinese Academy of Sciences, Beijing, China; European Organization for Nuclear Research (CERN), Geneva, Switzerland.
概括
本研究引入了一种机器学习模型,用于预测-129 (129I) 和-127 (127I) 度及其在海洋中的比率. 该框架为环境监测提供了准确的预测和不确定性估计.
科学领域:
- 环境科学 环境科学
- 核化学 核化学 核化学
- 海洋学 海洋学 海洋学
背景情况:
- 人类产生的-129 (I) 对于跟踪海洋流和污染至关重要.
- 目前的测量方法昂贵,并且提供有限的空间数据.
研究的目的:
- 开发一种机器学习框架,用于预测129I,127I和129I/127I比率.
- 为环境评估提供准确的预测与不确定性量化.
主要方法:
- 集成的机器学习框架使用贝叶斯神经网络 (BNN).
- 通过改进的雪算法 (ISGA) 进行超参数优化.
- 基于随时可用的海洋学参数进行预测.
主要成果:
- 在127I (R2=0.9623),129I/127I比率 (R2=0.9286) 和129I (R2=0.8148) 中实现了高预测精度.
- 对于预测,BNN提供了精心校准的不确定性间隔.
- ISGA-BNN框架在一个独立的测试集上表现出强的性能.
结论:
- 新的框架增强了放射性核酸的环境监测能力.
- 准确的,多目标预测与不确定性估计现在是可行的.
- 有潜力优化未来的海洋学采样活动.
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