广西师范大学学报（自然科学版）2026，Vol.44Issue(2)：115-131,17.DOI:10.16088/j.issn.1001-6600.2025041802
基于改进PatchTST的多步水质预测模型
A Multi-step Water Quality Prediction Model Based on Improved PatchTST
摘要
Abstract
Water pollution issues in China are becoming increasingly prominent,making improvements in the accuracy of water quality prediction models crucial for effective water resource management and ecological protection.This study addresses the challenges of complex nonlinear relationship modeling and computational efficiency in multi-step water quality time series prediction by proposing an improved PatchTST model.The model incorporates three key module optimizations:1)a lightweight CMixer encoder replacing the traditional Transformer encoder,which efficiently extracts temporal features through one-dimensional convolution and residual connections while reducing computational burden;2)an Adaptive Mid-Frequency Energy Optimizer(AMEO)that enhances mid-frequency spectral information,improving the model's ability to detect periodic changes in water quality parameters;and 3)a CKAHead prediction module based on Chebyshev polynomials and the Kolmogorov-Arnold representation theorem,strengthening the modeling of complex nonlinear relationships.In dissolved oxygen prediction at the Shimenzi section,the improved model achieves an MSE reduction of 12.9％compared with PatchTST and 14.0％compared with iTransformer,while maintaining a balance between computational efficiency and resource consumption.Furthermore,in generalization tests across five different monitoring sections,the model reduces MSE by approximately 10％compared with the next-best model for 48-hour forecasting tasks.Experimental results demonstrate that the improved model effectively enhances the accuracy and computational efficiency of multi-step water quality prediction,offering reference value for environmental time series analysis and water quality prediction research.关键词
水质预测/时序预测/PatchTST/深度学习/水质监测Key words
water quality prediction/time series forecasting/PatchTST/deep learning/water quality monitoring分类
资源环境引用本文复制引用
罗缘,朱文忠,王文,吴宇浩..基于改进PatchTST的多步水质预测模型[J].广西师范大学学报（自然科学版）,2026,44(2):115-131,17.基金项目
四川省科技计划重点研发项目(2023YFS0371) （2023YFS0371）
企业信息化与物联网测控技术四川省高校重点实验室开放基金(2024WYJ03) （2024WYJ03）
四川省智慧旅游研究基地(ZHYJ24-01) （ZHYJ24-01）
