• Sushant Sandanshiv
• Sakshi Petkar
• Sanket Mudekar
• Summit Rasal
• Dr. D.V.. Wadkar

Water quality modelling is required for proper water quality conservation and management. The limitation of fresh water sources suggests the need for water quality protection because it influences millions of people's lives. The need for increased accuracy in modelling water quality has motivated researchers to develop innovative models. Artificial neural networks (ANN) can identify the complex nonlinear relationships between input and output data. Biochemical Oxygen Demand (BOD) is an essential parameter for usage conditions of surface waters. The measurement of the wastewater BOD5 level requires five days, and using a prediction model to estimate BOD5 saves time and enables the adaptation of a modernized automated system. This study investigates the application of artificial neural networks (ANNs) in predicting the entering BOD5 concentration and the performance of WWTPs. The WWTP performance is defined in terms of the COD, BOD, and TSS concentrations in the effluent. Sensitivity analysis was performed to sort out the best-performing ANN network structure and configuration. The results showed that the ANN model created to predict the BOD concentration performed the best among the three outputs. The optimal performing neural network model that was obtained (three inputs – one output), indicated that the influent temperature and conductivity significantly affect the WWTP performance as inputs in all models

"PREDICTION OF WASTEWATER PARAMETERS USING ANN MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.k111-k116, May-2023, Available :http://www.jetir.org/papers/JETIR2305A15.pdf

"PREDICTION OF WASTEWATER PARAMETERS USING ANN MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppk111-k116, May-2023, Available at : http://www.jetir.org/papers/JETIR2305A15.pdf