机器学习和物联网用于水质监测的进步:全面审查
Ismail Essamlali1, Hasna Nhaila1, Mohamed El Khaili1
1Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Morocco.
Heliyon
|March 27, 2024
概括
实时水质监测由物联网 (IoT) 和机器学习 (ML) 增强. 这些技术使准确的预测和明智的决策能够保护水资源免受污染.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 持续监测水质对于公众健康和环境保护至关重要.
- 物联网 (IoT) 为环境传感提供实时数据采集能力.
- 机器学习 (ML) 为分析复杂的环境数据集提供了强大的工具.
研究的目的:
- 提供关于水质监测 (WQM) 当前进展的全面审查.
- 专注于WQM的物联网无线技术和ML技术的整合.
- 在这个跨学科领域确定挑战和未来的研究方向.
主要方法:
- 对WQM的物联网无线技术 (LpWAN,Wi-Fi,Zigbee,RFID,蜂,蓝牙) 现有文献的审查.
- 探索用于WQ数据分析的监督和无监督ML算法.
- 分析物联网数据流和ML预测能力之间的协同作用.
主要成果:
- 物联网可以为WQM提供高效的实时数据收集.
- ML技术 (监督和无监督) 有效地分析WQ数据以获得预测洞察力.
- 物联网和机器学习的整合促进了水资源管理的积极决策.
结论:
- 物联网和机器学习的结合代表了WQM的最先进状态.
- 有效的WQM依赖于利用各种物联网无线技术和先进的ML算法.
- 解决当前的挑战对于推进基于物联网-ML的WQM系统至关重要.
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