多视场深度学习和数据增强用于基于可见和近红外光谱的海洋沉积物中的重金属度预测
Xueying Li1, Wenzhuo Li1, Kai Li2
1School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China.
Marine pollution bulletin
|November 25, 2025
概括
这项研究引入了一种新的深度学习方法,将DR-WGAN-GP和MVF-LSTM结合起来,用于在海洋沉积物中准确预测重金属. 这种方法有效地解决了数据稀缺问题,改善了环境监测能力.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 海洋沉积物中的重金属污染对环境保护和生态系统健康构成风险.
- 有限的海洋沉积物数据阻碍了通用预测模型的发展.
- 非破坏性监测方法对于有效的环境评估至关重要.
研究的目的:
- 开发一种用于快速和非破坏性监测海洋沉积物中的重金属度 (Cu和As) 的新方法.
- 通过使用先进的深度学习技术,应对海洋沉积物分析中稀缺数据的挑战.
- 提高重金属预测模型的概括性和准确性.
主要方法:
- 建议将双重规范的瓦斯斯坦生成对抗网络 (DR-WGAN-GP) 融合为数据增强和多视野长短期内存 (MVF-LSTM) 进行预测.
- MVF-LSTM整合了来自不同视野的光谱频段,以创建一个多尺度模型.
- DR-WGAN-GP生成了高质量,多样化的样本数据,以增加有限的原始培训集.
主要成果:
- 使用原始样本,MVF-LSTM实现了使用Cu和As度预测的最高准确度.
- 结合的DR-WGAN-GP和MVF-LSTM模型在数据增强后显示出优异的预测结果.
- 获得的R2值为Cu的0.731和As的0.850,其中RMSE分别为2.674和1.228,RPD分别为1.934和2.613.
结论:
- 融合DR-WGAN-GP和MVF-LSTM提供了一种强大的方法,用于使用Vis-NIR光谱在海洋沉积物中的重金属预测.
- 该方法为环境领域的深度学习建模提供了一个新的技术途径,样本大小有限.
- 该研究通过改善海洋生态系统中的重金属监测,提高了环境保护的能力.
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