Modeling Wastewater Treatment Performance of a Vegetated Constructed Wetland Using Neural Network Approach

A multi-layer perceptron neural network model with Levenberg-Marquardt algorithm (MLP-LM) was developed based on the performance and operation data of a research worker named Marahatta on a full-scale vegetated submerged wetland system (VSB) operated for a 5 year period. influent chemical oxygen demand (CODinf), volatile suspended solids (VSSinf), total solids (TSinf) and temperature (T) were determined as the inputs of the model, whereas the output variables were one of the following; (i) effluent chemical oxygen demand (CODeff), (ii) total solids (TSeff) and (iii) volatile suspended solids (VSSeff). Multi-linear regression (MLR) and multi non-linear regression (MNLR) techniques were also used for data analysis to compare the prediction capability. Four criteria used for a statistical comparison were the following: mean square error (MSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R-2). The results showed that MLP-LM approach predicted the performance of the constructed wetland system than the MLR and MNLR techniques.