Abstract

The study develops a data-driven based model to predict biological oxygen demand of the treated wastewater from an institutional wastewater treatment plant. First, the data was collected for six parameters (i.e. pH, conductivity, temperature, total suspended solids, total organic carbon and biological oxygen demand) from the selected wastewater treatment plant by collecting and analysing samples over a period of more than two months. The quality parameters were then combined in seven different scenarios for training of machine learning models. These scenarios were tested using four machine learning models, i.e. multiple linear regression, artificial neural network, support vector machine and random forest. Finally, these models were compared to select the best model for prediction of biological oxygen demand using a set of indicators, i.e. coefficient of determination and root mean square error.

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