Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol
Publish place: Ecopersia، Vol: 7، Issue: 2
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_ECOPER-7-2_003
تاریخ نمایه سازی: 2 دی 1403
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: ۱۸۰ 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 R۲ ۰.۹۸ to ۱) and root mean square error or RMSE (۰.۷ to ۰.۰۱).
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Authors
S. Javan
Environmental Health Department, Medical Sciences Faculty, Neyshabur University of Medical Sciences, Neyshabur, Iran
A. Gholamalizadeh Ahangar
Soil Sciences Department, Soil & Water Engineering Faculty, Zabol University, Zabol, Iran
A.H. Hassani
Environmental Engineering Department, Environment & Energy Faculty, Tehran Science & Research Branch, Islamic Azad University, Tehran, Iran
J. Soltani
Water Engineering Department, Water Engineering Faculty, Abureyhan Campus, University of Tehran, Tehran, Iran
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