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一个基于云计算架构的物联网实时饮用水质量监测和预测模型

Rita Wiryasaputra^1,2, Chin-Yin Huang¹, Yu-Ju Lin¹

¹Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan.

Sensors (Basel, Switzerland)

|February 24, 2024

概括

这项研究开发了一种物联网 (IoT) 系统,用于实时监测饮用水质量. 一个决策树模型准确地预测了水安全,增强了公共卫生保护.

科学领域:

环境科学环境科学
计算机科学计算机科学
公共卫生公共卫生

背景情况:

确保安全饮用水对于可持续发展目标 (SDGs) 至关重要.
水基础设施问题可能会影响水质,味道,气味,外观和公共健康.
人力资源有限,需要自动化水质监测解决方案.

研究的目的:

开发基于物联网 (IoT) 的系统,用于实时监测水质.
应用机器学习技术来预测饮用水安全.
减少与污染水源相关的健康风险.

主要方法:

利用传感器网络收集水质数据 (温度,性,污染物).
采用Arduino微控制器和窄带IoT (NB-IoT) 来进行数据采集和传输.
实现了机器学习分类器 (决策树,梯度提升,随机森林,神经网络,支持矢量机) 用于水的可饮性预测.
通过使用Grafana的文本消息和基于Web的监控来实现集成的实时警报.

主要成果:

与其他机器学习方法相比,决策树 (DT) 模型在预测水的饮用性方面表现优越.
该系统成功监测了水质组件,并提供实时通知.
数据分为60%的培训,20%的验证和10%的测试比率用于模型评估.

结论:

开发的物联网系统有效地监测水质,并使用机器学习预测安全.
自动化监测和预测提高了当局高效管理水资源的能力.
定期取水样 (每两周一次) 可以根据系统的监测和预测结果进行指导.

关键词:

对于Arduino来说,这是一个很大的问题.物联网 (IoT) 的物联网 (IoT) 的物联网.二进制分类器二进制分类器饮用水 - 饮用水机器学习是机器学习.监控监控监控监控监控监控在通知通知通知时,请注意.

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