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基于道关注和TCN-BiGRU模型的废水质量预测
1School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng, 224051, Jiangsu Province, China. yuanjianbo224@126.com.
Environmental monitoring and assessment
|February 1, 2025
概括
一个新的CA-TCN-BiGRU模型准确地预测了多个水质指标,如化学氧气需求 (COD) 和总 (TP). 这种先进的深度学习方法通过精确的实时预测来增强水资源管理.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 环境监测中的人工智能
背景情况:
- 准确的水质预测对于有效的水资源管理和可持续实践至关重要.
- 化学氧气需求 (COD),悬浮固体 (SS),总 (TP),pH,总 (TN) 和氨 (NH3-N) 等关键指标需要可靠的预测.
- 现有的模型可能无法充分捕捉这些指标的复杂时间动态和相互依赖.
研究的目的:
- 开发和评估一种新的深度学习模型,CA-TCN-BiGRU,用于同时预测多个水质指标.
- 调查数据预处理和引导注意力机制对预测性能的影响.
- 将拟议的模型与其他用于预测水质的深度学习方法进行比较.
主要方法:
- 一个多输入多输出 (MIMO) 架构,将通道注意力 (CA) 与时间卷积网络 (TCN) 和双向门式循环单元 (BiGRU) 结合起来.
- 培训和测试CA-TCN-BiGRU模型,使用废水处理厂的数据.
- 与其他深度学习模型一起,对具有和没有数据预处理和道注意力的模型性能进行比较分析.
主要成果:
- 数据预处理显著提高了水质指标的预测准确性.
- 道注意力机制提高了模型专注于关键特征的能力.
- CA-TCN-BiGRU表现出卓越的性能,降低了COD,TP和SS的平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 约23%和26%,其中R2.2%增加了5.85%.
- 该模型具有稳定性和实时功能,适合短期 (1-3天) 预测.
结论:
- CA-TCN-BiGRU模型为多指标水质预测提供了一个高度准确和高效的解决方案.
- 它的低计算开销和快速推断速度使它成为实时水质监测系统的理想选择.
- 这些发现支持采用先进的深度学习技术,以改善水资源管理和环境保护.
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