Abstract
Prediction of the waste stabilization pond performance using linear multiple regression and multi-layer perceptron neural network: a case study of Birjand, Iran
Highlights
In recent decades, the application of computer modeling techniques has been introduced in many environmental issues [1]
Connections between nodes are presented by solid lines, which are associated with synaptic weights adjusted during the training procedure
Similar results was found for BODout in which an neural network (NN) model (H = 4, decay = 0.73 ± 0.03) predicts this parameter with lower error in comparison with Multiple regression (MR) regression
Summary
The application of computer modeling techniques has been introduced in many environmental issues [1]. Inappropriate operation of a waste water treatment systems may result in severe environmental and public health problems, as its effluent to a receiving. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P < 0.05) for the prediction of both effluent BOD5 and COD parameters. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Prediction of the waste stabilization pond performance using linear multiple regression and multi-layer perceptron neural network: a case study of Birjand, Iran.
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