确定最佳预测因素和采样频率,以使用随机森林开发营养软传感器.
Muhammad Arhab1, Jingshui Huang1
1Chair of Hydrology and River Basin Management, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
现在开发营养软传感器变得更加可行. 这项研究优化了预测因素的选择和采样频率,显示了有效的营养监测,减少了数据需求和成本.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 传感器技术 传感器技术
背景情况:
- 实时,现场营养监测仍然具有挑战性和成本.
- 由数据模型驱动的软传感器,为直接测量提供了一个有希望的替代方案.
- 软传感器开发的高数据要求带来了后勤障碍.
研究的目的:
- 优化营养软传感器的预测子集和采样频率.
- 使用随机森林模型开发有效的营养监测.
- 为了减少软传感器开发中的数据处理复杂性.
主要方法:
- 利用来自德国梅因河两座自动站的15分钟间隔水质数据.
- 采用随机森林模型,将溶解氧气,温度,导电性,pH,流量和时间特征作为预测因素.
- 应用前向子集选择和膝关节点的优化确定.
主要成果:
- 模型实现了酸盐,正酸盐和的R2>0.95,具有最佳预测因素.
- 增加采样频率提高了模型性能 (RMSE).
- 确定了最佳的采样频率:酸盐 (3.6/2.8小时),酸盐 (2.4/1.8小时), (2.2小时).
结论:
- 营养软传感器是有效的水质监测.
- 优化模型在较少的预测因素和较低的采样频率下运行良好.
- 这种方法减少了环境监测中的数据处理负担和成本.
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