An interpretable multi-task learning model for effluent quality and greenhouse gas emissions prediction in wastewater treatment plants

Wastewater treatment plants (WWTPs) face growing pressure to comply with regulatory effluent standards while reducing greenhouse gas (GHG) emissions as part of the net-zero and sustainable transformation. Recent advances in deep learning have improved WWTP forecasting. However, most studies remain focused on single outputs (e.g., one effluent parameter) and/or single-step prediction, with limited attention to GHG emissions and the interactions among concurrently predicted targets. In this study, an attention-enhanced multi-gate mixture-of-experts (AttMMoE) model is proposed for multi-task, multi-step prediction of effluent quality and GHG emissions in WWTPs. This model is trained to predict WWTP effluent parameters - TN, NH 4 + , TSS, BOD 5 , COD - and GHG emissions, validated on BSM2G datasets. Compared with baseline models - LSTM, GRU and Transformer, AttMMoE yields higher Coefficient of determination (R 2 ), Pearson correlation coefficient ( r ) and lower Root mean square error (RMSE) across tasks and horizons, with better performance at extended prediction horizons. Ablation analysis confirms that both the multi-head self-attention and task-specific multi-gate mechanism contribute to these improvements. A comparison of single-task learning (STL), multi-output joint learning (MJL), and multi-task learning (MTL) based on task correlation demonstrates that the advantages of joint learning increase gradually with task correlation . The proposed model performs better, reducing cross-task interference among heterogeneous tasks. Finally, gating analysis reveals patterns consistent with known treatment processes, supporting associative, model-internal interpretability for multi-output forecasting of effluent quality and GHG emissions. Overall, through the multi-gate structure, AttMMoE not only improves cross-task forecasting performance by mitigating negative transfer, but also offers endogenous interpretability, enabling accurate forecasting of effluent quality and GHG emissions to support net-zero WWTP operation.