一种新方法用于快速识别雨水管道网络中的外部水类,使用UV-Vis吸收光谱学
Xiaowei Chen1, Nanjing Zhao1, Wanjiang Zhu2
1Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine, Mechanics, Chinese Academy of Sciences, Hefei, China.
概括
本研究介绍了一种使用生成对抗网络 (GAN) 和长短期记忆 (LSTM) 的新方法,用于准确识别雨水管道中的外部水源. 该方法在最小的样本中实现了高精度,使得快速可追溯性分析成为可能.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 紫外可见 (UV-Vis) 吸收光谱技术提供了灵敏的实时监测,用于识别雨水网络中的外部水.
- 挑战包括有限的真实样本和复杂的废水组成,阻碍了准确的可追溯性.
- 现有的方法因数据不足和复杂的分析而扎.
研究的目的:
- 开发一种新的方法来识别雨水管道网络中的外部水源,使用有限的样本.
- 提高对外部水入侵的可追溯性分析的准确性和效率.
- 利用先进的机器学习算法进行光谱数据处理和水源识别.
主要方法:
- 生成对抗网络 (GAN) 用于合成光谱数据的生成.
- 用于光谱数据预处理的多倍散射校正 (MSC).
- 变化模式分解 (VMD) 用于光谱分解和重组.
- 长短期记忆 (LSTM) 用于建立识别模型和确定最佳分解参数 (K).
主要成果:
- 拟议的方法在识别家用污水,地表水和工业废水方面达到98.81%的准确性.
- 最佳光谱分解 (K=5) 显著提高了识别准确性.
- 使用混合水样品的验证表明,在确定雨水中的外部水源时,准确率为98.99%.
- 检测时间缩短到10秒之内.
结论:
- 集成的GAN-MSC-VMD-LSTM方法为雨水管道网络中的快速外部水识别提供了强大的解决方案.
- 该方法有效地克服了样本不足和复杂的水组成的局限性.
- 这种技术为实时监控和水污染事件的可追溯性提供了潜在的突破.
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