Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach

In this study, three single Artificial Intelligence (AI) based models i.e., Back Propagation Neural Network (BPNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and a linear Auto Regressive Integrated Moving Average (ARIMA) model as well as three different ensemble techniques i.e., Simple average ensemble (SAE), weighted average ensemble (WAE) and neural network ensemble (NNE) are applied for single and multi-step ahead modeling of dissolve oxygen (DO) in the Yamuna River, India. In this context, DO, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Discharge (Q), pH, Ammonia (NH3), Water Temperature (WT) data for three different stations i.e., Hathnikund (SL1), Nizamuddin (SL2) and Udi (SL3) recorded by central pollution control board were used. The performance accuracy of the models was determined using Determination Coefficient (DC) and Root Mean Square Error (RMSE). The obtained results of the single models showed that, ANFIS model outperformed all other three models and increased averagely up to 7% and 19% for SL1 and SL2 in performance accuracy while for SL3, SVM model performed better than other models and increased the average performance up to 16%. In the ensemble techniques, the results showed that, for all the three stations, NNE could increase the average performance by single models up to 14% in the verification phase. This justified the reliability and robustness of NNE in multi-step ahead modeling of DO due to its promising ability in solving nonlinear processes.