Javan, S., Gholamalizadeh Ahangar, A., Hassani, A.H., Soltani, J. (2019). Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol. , 7(2), 87-95.
S. Javan; A. Gholamalizadeh Ahangar; A.H. Hassani; J. Soltani. "Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol". , 7, 2, 2019, 87-95.
Javan, S., Gholamalizadeh Ahangar, A., Hassani, A.H., Soltani, J. (2019). 'Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol', , 7(2), pp. 87-95.
Javan, S., Gholamalizadeh Ahangar, A., Hassani, A.H., Soltani, J. Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol. , 2019; 7(2): 87-95.

Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol

ECOPERSIA
Article 3, Volume 7, Issue 2 - Serial Number 28, 2019, Pages 87-95    PDF (548.88 K)
Document Type: Original Research
Authors
S. Javan1; A. Gholamalizadeh Ahangar* 2; A.H. Hassani3; J. Soltani4
1Environmental Health Department, Medical Sciences Faculty, Neyshabur University of Medical Sciences, Neyshabur, Iran
2Soil Sciences Department, Soil & Water Engineering Faculty, Zabol University, Zabol, Iran
3Environmental Engineering Department, Environment & Energy Faculty, Tehran Science & Research Branch, Islamic Azad University, Tehran, Iran
4Water Engineering Department, Water Engineering Faculty, Abureyhan Campus, University of Tehran, Tehran, Iran
Abstract
Aims: Artificial Neural Networks (ANNs) are powerful tools that are commonly used today in prediction deposit-related sciences. The research aimed at predicting various five links of heavy metals using the properties of deposit.

Materials and Methods: 180 samples of surface sediments were taken from the Chahnimeh reservoir and they were transferred to under standard conditions. Total Zinc concentration, deposit properties and Zinc five bonds with deposit were measured. Efficiency of the ANN and Perceptron (MLP) model to estimate the Zn following the measurement of parameters in the laboratory.

Findings: Five links were predicted with the aid of ANNs and MLP model. Deposit properties and total concentrations of heavy metals were considered as input and each of bonds were considered as output.

Conclusion: Ultimately, the ANN showed good performance in the predicting the determination of coefficients or R2 0.98 to 1) and root mean square error or RMSE (0.7 to 0.01).
Keywords
artificial neural networks; Heavy metals; Sediment Pollution; Chahnimeh
References
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