Vectorized Evidential Reasoning-Based Multivariate Effluent Quality Prediction for Sustainable Wastewater Treatment Process

Accurate prediction of multivariate effluent quality is essential for achieving reliable operation and sustainable management of wastewater treatment processes (WWTPs). However, the strong nonlinearity, coupling relationships, and non-prioritized multi-input multi-output (MIMO) characteristics of WWTP pose significant challenges to conventional prediction methods. To address these issues, a vectorized evidential reasoning-based multivariate effluent quality (VER-MEQ) prediction method is proposed. First, a VER model is developed, in which the nonlinear mapping between multiple process variables and multiple effluent quality indicators is established through a vector evidence matrix (VEM), enabling simultaneous online prediction of multiple outputs within a unified inference framework. Subsequently, a structured hybrid initialization (SHI) strategy is introduced to improve the initialization quality of the genetic algorithm, and the VER inference process is incorporated into parameter optimization to enable online model parameter updating, thereby improving prediction performance. The proposed method is validated under sunny, rainy, and stormy operating scenarios. Experimental results demonstrate that VER-MEQ achieves competitive prediction accuracy, provides a transparent belief-based inference process, and maintains preliminary anti-interference performance under the tested conditions. By providing transparent and credible prediction results for effluent ammonia nitrogen (NH 3 -N e ) and total nitrogen (TN e ), the proposed framework can support proactive operational decision-making, improve effluent compliance, reduce the risk of nutrient discharge, and contribute to the sustainable operation of WWTPs.