华南理工大学学报（自然科学版）2026，Vol.54Issue(1)：60-69,10.DOI:10.12141/j.issn.1000-565X.250132
面向污水处理的轻量化多参数时序预测方法
A Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment
摘要
Abstract
In wastewater treatment processes,the efficient modeling of key water quality parameters is crucial for achieving process control optimization,anomaly detection,and decision support.However,the process data generally exhibits characteristics such as temporal dependence,multivariable coupling,and non-stationarity under varying operating conditions,posing significant challenges to accurate modeling.To address these issues,this paper proposes a Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment based on the Stationary Wavelet Transform(SWT)and Collaborative Attention(CA)mechanism.This model first performed multi-scale decomposition on wastewater data and used the stationary wavelet transform to extract data features from sequences at different scales.Subsequently,a collaborative attention mechanism based on geometric attention and sparse attention was constructed to effectively capture the complex coupling relationships and temporal features among key water quality parameters.Finally,the features reconstructed via inverse wavelet transform were mapped to the final prediction results through a dual-prediction layer.The model was trained and validated on a measured dataset from a wastewater treatment plant in Dongguan,with multi-step prediction tasks and partial data visualization analyses conducted.Experimental results show that,in the 24-step multi-output prediction tasks,the proposed model achieves a reduction of 9.15%to 37.70%in multi-output root mean square deviation(RMSSD)compared to benchmark models.In other prediction tasks,its accuracy ranks second only to TimesNet,which has a significantly larger parameter scale.These results demonstrate an effective balance between lightweight design and high accuracy,thereby validating the efficacy of the proposed model for time-series prediction in wastewater treatment.关键词
多参数时序预测/平稳小波变换/协同注意力机制/轻量化/污水处理Key words
multivariate time series prediction/stationary wavelet transform/collaborative attention mechanism/lightweight/wastewater treatment分类
信息技术与安全科学引用本文复制引用
唐莉丽,刘乙奇..面向污水处理的轻量化多参数时序预测方法[J].华南理工大学学报（自然科学版）,2026,54(1):60-69,10.基金项目
国家自然科学基金项目(92467106,62273151,62073145) （92467106,62273151,62073145）
广东省基础与应用基础研究基金项目(2021B1515420003) （2021B1515420003）
广东省普通高校创新团队项目(2023KCXTDO72) （2023KCXTDO72）
先进造纸联合实验室开放课题(20241645)Supported by the National Natural Science Foundation of China(92467106,62273151,62073145),the Guangdong Basic and Applied Basic Research Foundation(2021B1515420003)and the General College Innovation Team Project of Guangdong Province(2023KCXTDO72) （20241645）
