Estimation of Water Disinfection by Using Data Mining
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Al-Zaytoonah University of Jordan, Amman, Jordan
Shahnaz Alkhalil   

Al-Zaytoonah University of Jordan
Publication date: 2021-01-05
Ecol. Eng. Environ. Technol. 2021; 1:109–116
In this study, the artificial neural network (ANN) models and multiple linear regression techniques were used to estimate the relation between the concentration of total coliform, E. coli and Pseudomonas in the wastewater and the input variables. Two techniques were used to achieve this objective. The first is a classical technique with multiple linear regression models, while the second one is data mining with two types of ANN (Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The work was conducted using (SPSS) software. The obtained estimated results were verified against the measured data and it was found that data mining by using the Radial Basis Function (RBF) model has good ability to recognize the relation between the input and output variables, while the statistical error analysis showed the accuracy of data mining by using the RBF model is acceptable. On the other hand, the obtained results indicate that MLP and multiple linear regression have the least ability for estimating the concentration of total coliform, E. coli and pseudomonas in wastewater.