Turkish Journal of Electrical Engineering and Computer Sciences




The basis of a wastewater treatment system is to achieve the desired characteristics of the wastewater treatment process. An estimation of the obtained wastewater treatment characteristics provides the information needed to set up the current process steps, and it is important to have an optimum treatment. In this study, an artificial immune system (AIS) structure is developed to estimate important wastewater output parameters such as pH, DBO, DQO, and SS for the first time. The proposed AIS models are based on the clonal selection principle, and the dataset is provided from the University of California Irvine (UCI) Machine Learning Library. The current dataset is analyzed by principal component analysis (PCA) to obtain maximum system performance. As a result of the simulation, the output parameters are successfully predicted using the AIS model with real data. The classifier's performance ratios are studied separately using the coefficient of determination (R$^{2})$ and the mean squared error of prediction (MSEP), and their rates are given in this study.


Wastewater, principal component analysis, artificial immune system, coefficient of determination, mean squared error, classification

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