RT - Journal Article T1 - Estimation of Zn Bonds Using Multi-Layer Perceptron (MLP) Artificial Neural Network Method in Chahnimeh, Zabol JF - mdrsjrns YR - 2019 JO - mdrsjrns VO - 7 IS - 2 UR - http://ecopersia.modares.ac.ir/article-24-17907-en.html SP - 87 EP - 95 K1 - Artificial Neural Networks K1 - Heavy Metals K1 - Sediment Pollution K1 - Chahnimeh AB - 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). LA eng UL http://ecopersia.modares.ac.ir/article-24-17907-en.html M3 ER -