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用于预测病毒度和评估废水矩阵中的清除效率的零射击概括

Jianxu Chen¹, Ibrahima N'Doye^2,3, Mohammad Khalil Monjed⁴

¹Environmental Science and Engineering Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia.

Scientific reports

|November 25, 2025

概括

预测废水处理中的病毒颗粒是具有挑战性的,因为过程漂移. 一个新的双重注意长期短期记忆网络 (DA-LSTM) 模型有效地预测了不同废水矩阵的病毒负载,提高了准确性和概括性.

科学领域:

环境微生物学环境微生物学
废水处理技术废水处理技术
病毒学病毒学

背景情况:

由于工艺变化,在废水处理厂 (WWTP) 中难以预测病毒颗粒.
现有的方法在各种废水矩阵 (WM) 中与未见的数据作斗争.

研究的目的:

开发一个准确和强大的模型,用于预测基于有氧膜生物反应器 (AeMBR) 的WWTP中的病毒颗粒.
评估模型在处理废水处理漂流方面的性能,并将其推广到未见的WMs.

主要方法:

建议使用马尔科夫链蒙特卡洛 (MCM),马尔科夫链和多变量高斯式 (MMCM),高斯式混合 (GMM) 和Copula (CM) 模型进行数据增强.
开发了一个双重注意长期短期记忆网络 (DA-LSTM) 框架,集成病毒粒子预测的生成模型.
在沙特阿拉伯的WWTP中测试了DA-LSTM模型,用于预测胡温和斑点病毒,总病毒和腺病毒.

主要成果:

该DA-LSTM模型在未见的WMs和废水漂流中展示了显著的适应性和强大的性能.
使用MMCM实现了高零射击泛化性能,在沙子和MBR矩阵中产生0.91和0.97的R2值,在化废水中产生0.97的R2值.
在不同地区的市政WWTP中预测病毒颗粒的有效性得到证实,增强了区域零射击泛化.

结论:

DA-LSTM框架与MMCM等生成模型相结合,为WWTP中的病毒粒子预测提供了一个强大的解决方案.
该模型有效地处理过程漂移,并将其推广到新的废水矩阵,这对于公共卫生监测至关重要.
这种方法提高了预测病毒载荷的能力,并评估了在各种废水处理场景中的清除效率.

关键词:

双重注意长时间短期记忆网络废水流动的过程漂移.生成型模型是一种生成型模型.记录删除值的日志删除值.病毒粒子预测预测废水矩阵废水矩阵零射击概括的一般化

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