Evolving Deep Delay Echo State Network for Effluent NH4-N Prediction in Wastewater Treatment Plants

In wastewater treatment plants (WWTPs), the prediction of effluent ammonia nitrogen (NH4-N) concentration is vital, which is a major cause of lake eutrophication. To solve this problem, the evolving deep delay echo state network (EDDESN) is proposed. Firstly, the EDDESN is decomposed into several serially connected sub-reservoirs, which are inserted delay units to learn the temporal relationships within sequence data. Secondly, the input and reservoir internal weights are generated by a singular value decomposition-based matrix design strategy, which can reduce searching dimensions and guarantee the echo state property (ESP). Moreover, the architecture hyperparameters and weights of EDDESN are simultaneously optimized by a competitive swarm optimizer (CSO)-based two-stage optimization approach. Finally, the experimental results on practical NH4-N dataset and simulated Mackey–Glass time series demonstrate the superiority of EDDESN as compared with other time series prediction approaches.