使用卷积神经网络和封闭循环单元进行全面的河水质量监测:沿瓦伊盖河的一项案例研究
Geetha T S1, Gandhimathi G2, Chellaswamy C2
1Department of Electronics and Communication Engineering, SA Engineering College, Chennai, 600077, India.
Journal of environmental management
|July 2, 2024
概括
使用卷积神经网络和门式循环单元 (CNGRU-WQM) 的新系统有效监测河流水质. 这种先进的系统提供实时数据和警报,改善水资源管理和环境保护.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 有效的河水质量监测对于可持续的水资源管理至关重要.
- 现有的方法可能缺乏实时功能和全面的参数覆盖.
研究的目的:
- 开发和验证一个全面的实时河流水质监测系统.
- 通过知情决策来加强环境保护.
主要方法:
- 实现一个卷积神经网络和门式循环单位水质监测 (CNGRU-WQM) 模型.
- 传感器的战略布置可用于实时收集诸如度,pH值,温度和总溶解固体等参数的数据.
- 开发一个具有超出预先设定的水质标准的实时警报系统.
主要成果:
- CNGRU-WQM模型实现了高的验证准确性,达到97.86%.
- 该系统成功地收集了多个关键水质指标的实时数据.
- 集成实时警报,以通知利益相关者潜在的水质问题.
结论:
- 开发的CNGRU-WQM系统为河流水质监测提供了高效和准确的解决方案.
- 该系统为环境保护和及时干预水资源管理提供了宝贵的工具.
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