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• W4214894051 abstract "• Oil pollution has been one of the most common environmental damages in recent decades. • Polycyclic aromatic hydrocarbons are one of the most common hazardous pollutants . • The back propagation ANN was used for movement model of PAHs in groundwater. • Oil dispersion and total suspended solids have more important in estimating of PAHs. Oil pollution has been one of the most common environmental damages in recent decades, which has been abundant in oil-producing countries such as Iran. This study aimed at using the artificial neural network (ANN) to predict the amount of petroleum pollutants including polycyclic aromatic hydrocarbons (PAHs) in the groundwater of the Tehran Oil Refinery by a set of input features, which modeled in MATLAB R2015a. Accordingly, 13 input features were used for developing an ANN model as inputs, including pH values, electrical conductivity, total dissolved solids, and total suspended solids (TDS and TSS), distance from the source of contamination (Distance), the amount of phenol, oil and grease, and the amount of ions such as potassium, sodium, chlorine, sulfate, magnesium, and calcium. The testing and training data for learning the ANN model were produced by applying 45 data obtained from the analyzing of sample wells around the Tehran refinery in the laboratory. Due to the low number of data, back-propagation networks were employed for modeling. The regression values for training, validation, and test data were equal to 0.997, 0.991, and 0.917, respectively. Based on the results, the best validation performance was detected in epoch 13. The mean square errors were 0.00348, indicating the high accuracy of the ANN method in the prediction and estimation of PAHs in the mentioned site, and thus this model can be used on sites with similar conditions." @default.
• W4214894051 created "2022-03-05" @default.
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• W4214894051 date "2022-12-01" @default.
• W4214894051 modified "2023-09-27" @default.
• W4214894051 title "Application of artificial neural network with the back-propagation algorithm for estimating the amount of polycyclic aromatic hydrocarbons in Tehran Oil Refinery, Iran" @default.
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• W4214894051 doi "https://doi.org/10.1016/j.enmm.2022.100677" @default.
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