Volume 7, Issue 2 (2019)
ECOPERSIA 2019, 7(2): 87-95 |
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Javan S, Gholamalizadeh Ahangar A, Hassani A, Soltani J. Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol. ECOPERSIA 2019; 7 (2) :87-95
URL: http://ecopersia.modares.ac.ir/article-24-17907-en.html
URL: http://ecopersia.modares.ac.ir/article-24-17907-en.html
1- Environmental Health Department, Medical Sciences Faculty, Neyshabur University of Medical Sciences, Neyshabur, Iran
2- Soil Sciences Department, Soil & Water Engineering Faculty, Zabol University, Zabol, Iran , ahangar@uoz.ac.ir
3- Environmental Engineering Department, Environment & Energy Faculty, Tehran Science & Research Branch, Islamic Azad University, Tehran, Iran
4- Water Engineering Department, Water Engineering Faculty, Abureyhan Campus, University of Tehran, Tehran, Iran
2- Soil Sciences Department, Soil & Water Engineering Faculty, Zabol University, Zabol, Iran , ahangar@uoz.ac.ir
3- Environmental Engineering Department, Environment & Energy Faculty, Tehran Science & Research Branch, Islamic Azad University, Tehran, Iran
4- Water Engineering Department, Water Engineering Faculty, Abureyhan Campus, University of Tehran, Tehran, Iran
Abstract: (6385 Views)
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).
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).
Article Type: Original Research |
Subject:
Pollution (Soil, Water and Air)
Received: 2018/03/18 | Accepted: 2019/02/3 | Published: 2019/04/15
Received: 2018/03/18 | Accepted: 2019/02/3 | Published: 2019/04/15
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