机器学习构建颜色特征,以加快长期连续水质监测的发展
Mengyuan Zhang1, Yanquan Huang1, Dongsheng Xie1
1School of Environmental Science and Engineering South China University of Technology, Guangzhou 510006, China.
Journal of hazardous materials
|October 6, 2023
概括
本研究介绍了用于长期连续水质监测 (LTCM) 的图像识别方法. 它通过分析颜色变化来准确检测污染物度,消除了对传感器的需求.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 计算机视觉 计算机视觉
背景情况:
- 长期持续水质监测 (LTCM) 对于水资源安全至关重要.
- 目前基于实验室的机器学习 (ML) 污染物检测依赖于传感器,这些传感器对LTCM有局限性.
研究的目的:
- 开发一种使用图像识别进行LTCM的新型无传感器方法.
- 为了确定污染物度和水样中的颜色变化之间的关系.
主要方法:
- 利用图像识别来分析水样中的微妙颜色变化.
- 采用K-means集群和RGB平均特征,从原始像素数据中提取颜色变化特征.
- 应用了四种ML模型 (XGBoost,Linear,SVR,Ridge) 来进行污染物度预测.
主要成果:
- 与主要成分分析 (PCA) 相比,确定系数 (R2) 提高了多达95.9%.
- 在预测真实废水和水样中的模拟污染物 (Cu2+,Co2+,Rhodamine B) 和Cr(VI) 度时,达到95%以上的R2.
- 在没有样品预处理的情况下,证明有效捕捉无形的颜色变化.
结论:
- 拟议的图像识别方法为LTCM提供了一种可靠的,无传感器的方法.
- 这种技术可以通过分析视觉颜色变化来准确量化污染物度.
- 在没有复杂仪器的情况下,为持续的水质评估提供了有价值的工具.
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