整合深度学习技术,以有效监测和管理河水质量
Chellaswamy Chellaiah1, Sriram Anbalagan1, Dilipkumar Swaminathan2
1Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, 621105, India.
Journal of environmental management
|September 20, 2024
概括
一个新的混合AI模型使用实时数据准确监测河流水质量. 该系统为可持续的水资源管理和环境保护提供及时的警报.
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或全面的参数覆盖.
- 卡维里河需要强有力的监测,以确保生态健康和资源可持续性.
研究的目的:
- 建立一个全面的,实时的河流水质监测系统.
- 开发和验证一种新的混合人工智能模型,用于准确的水质评估.
- 为了实现及时干预环境保护和知情的水资源管理.
主要方法:
- 部署传感器网络,以实时收集诸如度,pH值,温度和总溶解固体 (TDS) 等参数的数据.
- 将数据传输到基于云的网页门户,用于存储和分析.
- 混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型的应用用于水质评估.
主要成果:
- 监测系统成功地以5分钟的间隔捕获了关键水质参数的实时数据.
- 拟议的CNN-LSTM模型实现了98.40%的高验证精度.
- 该系统通过超越水质值的实时警报,证明了其实际实用性.
结论:
- 开发的监测系统提供了对河流水质的全面和准确评估.
- 与现有方法相比,混合CNN-LSTM模型提供了卓越的性能.
- 该系统是做出明智决策,及时干预和环境保护工作的宝贵工具.
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