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预测监测,识别和控制饮用水源盆地中微囊的开花:一种整合的人工智能和生物信息学方法
Hiep T Nguyen1, Jonathan Wijaya1, Jaehyung Kim1
1Department of Civil Engineering, College of Engineering, Kyung Hee University, Yongin, Republic of Korea.
Journal of hazardous materials
|January 31, 2026
概括
这项研究引入了一个人工智能驱动的框架,用于在饮用水源中主动监测有害藻类繁殖 (HAB). 它将人工智能预测与生物信息诊断相结合,用于预警和有效的水资源管理.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 计算生物学 计算生物学
背景情况:
- 有害藻类繁殖 (HAB) 对饮用水设施构成重大挑战.
- 目前对源水中HAB的监测方法主要是反应性的,导致反应延迟.
研究的目的:
- 开发和验证一个综合框架,以积极监测饮用水源中的HAB.
- 将饮用水安全从反应性管理转变为主动,基于风险的控制策略.
主要方法:
- 利用人工智能 (AI) 模型,将环境特征纳入HAB预测中.
- 采用了带有短读和长读序列的族群基因组分析,用于应变解析的诊断.
- 在饮用水处理厂进行了现场调查和生物信息分析.
主要成果:
- 人工智能模型实现了高预测准确性 (R2高达0.9) 与HAB事件的1周领先时间.
- 可解释的人工智能确定有机和化合物是HAB的关键预测因素.
- 在处理厂确认了Microcystis的完全去除,其特征通过生物信息学验证.
结论:
- 该研究提出了源水管理的综合预测监测和诊断框架.
- 这一框架支持一个转向主动,基于风险的饮用水安全控制的范式转变.
- 这些发现使HAB的管理更有效,确保更安全的饮用水供应.
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