溶解氧杆基础模型的可解释预测,用于农村废水处理系统的主动通风
Jeimy L Martinez De La Hoz1, Mahathir Mohammad Bappy1, Md Shafikul Islam1
1Louisiana State University, Department of Mechanical and Industrial Engineering, Baton Rouge, LA, 70803, United States.
Water research
|November 26, 2025
概括
现在可以使用一种新的变压器基础模型在农村废水系统中准确预测溶解氧 (DO). 这种可解释的框架显著提高了预测准确度,以改善运营规划和能源效率.
科学领域:
- 环境工程 环境工程
- 废水处理 废水处理
- 环境科学中的人工智能
背景情况:
- 准确的溶氧 (DO) 预测对于生物废水处理效率和能源管理至关重要.
- 农村稳定池面临着独特的挑战,原因是DO对环境因素和操作变化的非线性反应.
- 现有的预测方法与农村废水系统典型的复杂性和数据限制作斗争.
研究的目的:
- 开发一个可解释的预测框架,用于农村废水系统中溶解氧 (DO).
- 利用基于变压器的基础模型来提高时间序列预测的准确性.
- 为运营规划和可持续的系统管理提供决策一致的见解.
主要方法:
- 开发了一个基于变压器的基础模型用于时间序列预测,在多变量传感器数据上进行微调.
- 训练了四个季节性模型 (春季,夏季,秋季,冬季),具有24小时的预测时间.
- 集成的SHapley添加式解释 (SHAP) 用于模型解释性和对"如果"情景的敏感性分析.
主要成果:
- 与基线 (16-46%) 相比,在24小时的时间范围内,对称平均绝对百分比误差 (SMAPE) 显著降低至7%以下.
- 使用SHAP识别了特定于模式的关键DO驱动因素,包括pH,导电性,温度,度和氨.
- 与经典机器学习 (SVR,XGBoost) 和深度学习 (LSTM,TFT) 的基准标准相比,已经证明了卓越的性能.
结论:
- 拟议的可解释预测框架显著提高了在数据受限的农村废水系统中DO预测的准确性.
- 基础建模,季节性细分和基于SHAP的可解释性对于有效的废水管理至关重要.
- 该框架为积极的通风规划和可持续的分散废水处理提供了一种透明和可操作的方法.
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