用传感器数据智能预测藻类度,以生产更安全的饮用水
Han Yu1,2, Jing Li2, Linda Holmer3
1Swerim AB, Process Metallurgy, SE-971 25 Luleå, Sweden.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
一个新的AI扫描聚焦过程改善了藻类预测,以获得更安全的饮用水. 日期-藻类-温度-pH (DATH) 模型表现出优于其他方法的性能,提高了水质预测.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 预测藻类繁殖对于生产安全饮用水至关重要.
- 传统的方法难以及时准确地预测水质参数.
研究的目的:
- 开发和评估一种新的AI扫描聚焦过程,以改进藻类数量的模拟和预测.
- 确定影响水质的关键因素,并优化预测模型.
主要方法:
- 使用前神经网络 (FNN) 基础,系统地扫描隐藏层配置和因素组合.
- 包括诸如日期,传感器数据 (温度,pH,导电,度,UV254溶解有机物),实验室测量 (藻类度) 和计算的二氧化碳度等因素.
- 将开发的AI模型 (DATH,DATC) 与简单的传统神经网络 (SP) 和盲人AI训练过程 (BP) 相比较.
主要成果:
- 人工智能扫描聚焦过程确定了最佳模型,包括日期-藻类-温度-pH (DATH) 和日期-藻类-温度-CO2 (DATC).
- DATH和SP模型在藻类和水质因子预测方面显示了可比的结果,比盲目过程 (BP) 更好.
- 在应用测试中,DATH在应用测试中表现优于SP,在延长训练后保持准确性.
结论:
- 人工智能扫描聚焦过程有效地识别了提高水质预测的关键因素.
- 这种方法为改善水质和环境监测中的数值预测提供了一个新的方法.
- DATH模型显示了在确保更安全的饮用水生产方面实际应用的巨大潜力.
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