An Adaptive Multi-Scale Graph and Seasonal Decomposition LSTM Framework for Accurate Wastewater Effluent Forecasting
TL;DR
This study introduces MGCSD-LSTM, a novel framework combining multi-scale graph convolution and seasonal decomposition with LSTM to improve long-term wastewater effluent forecasting, addressing challenges from sudden water-quality fluctuations.
An Adaptive Multi-Scale Graph and Seasonal Decomposition LSTM Framework for Accurate Wastewater Effluent Forecasting
Yifan Wu; Jiashuo Meng; Yongze Liu; Xiaoyu Zhang
https://doi.org/10.1109/ACCESS.2025.3649919
Volume 14
Predicting effluent indicators is crucial for enabling a wastewater treatment plant (WWTP) to actively adjust treatment processes. However, research on long-term sequence forecasting under sudden water-quality fluctuations remains scarce. In this study, we propose a novel framework called multi-scale graph convolutional seasonal decomposition LSTM (MGCSD-LSTM) to enhance effluent prediction. It in...