CABNas-nir:在NAS框架和主动学习的融合算法上对城市管道网络污泥进行近红外分类
Yuxi Yang1, Li Fu2, Qingjun Wei3
1POWERCHINA Eco-Environmental Group Co., Ltd., Shenzhen, China.
PloS one
|December 19, 2025
概括
一种新的深度学习方法,CABNas-nir,使用近红外光谱学准确识别管道网络污泥来源. 这种方法优于环境监测和污染控制的传统方法.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 计算机科学 计算机科学
背景情况:
- 管道网络污泥是城市污水系统中复杂的污染物,需要精确的来源分类才能有效管理.
- 传统的污泥分析方法耗时且效率低下.
- 近红外光谱 (NIR) 显示出有希望的结果,但面临着诸如光谱冗余和有限数据等挑战.
研究的目的:
- 开发一个先进的深度学习模型,用于准确和高效的管道网络污泥来源分类.
- 解决污泥分析中传统方法和NIR光谱学的局限性.
主要方法:
- 提出了CABNas-nir,一个使用神经架构搜索 (NAS) 的深度神经网络.
- 综合竞争性适应性重权取样 (CARS) 用于特征选择,基线漂移增强用于小样本问题,以及积极学习 (AL) 用于样本选择.
- 采用通过NAS优化的卷积神经网络 (CNN) +长短期记忆 (LSTM) 架构.
主要成果:
- 在污泥源分类中实现了92.86%的准确性,显著优于支持向量机 (SVM) 和极端梯度提升 (XGBoost).
- 沙普利添加式扩展 (SHAP) 分析确定了关键的光谱区域 (1400-1700 nm),有助于准确的分类.
- 在识别管道网络污泥来源方面表现出增强的稳定性.
结论:
- CABNas-nir提供了一个强大而准确的解决方案,用于管道网络污泥来源的识别.
- 该研究为城市污水系统的快速和精确的环境监测奠定了基础.
- 综合方法克服了现有方法的局限性,为改善污染控制和资源回收铺平了道路.
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