Modeling Qualitative Parameters of SAR, EC, and TDS in Groundwater using Optimized Artificial Neural Network Model (Case Study: Behbahan Plain)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ahanin Jan Kimia; EGDERNEZHAD ASLAN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ENVIRONMENT AND WATER ENGINEERING Year:2020 | Volume:6 | Issue:2 Page(s): 161-172

Download Full-Text

Persian Verion

View:

671

Download:

Cites:

Information Journal Paper

Title

Modeling Qualitative Parameters of SAR, EC, and TDS in Groundwater using Optimized Artificial Neural Network Model (Case Study: Behbahan Plain)

Author(s)

Ahanin Jan Kimia | EGDERNEZHAD ASLAN | Issue Writer Certificate

Keywords

Artificial Neural Networks Model

Groundwater

Quality Parameters

Simulation

Abstract

Proper modeling of Groundwater quality is an important tool for planning and decision making in water resources management. The present study was conducted to simulate the Groundwater Quality Parameters of Behbahan Plain including SAR, EC, and TDS using ANN and ANN + PSO models and finally to compare their results with the measured data. Input information to the models gathered were for TDS quality parameter including electrical conductivity, absorption ratio of sodium, sulfate, calcium, magnesium and sodium, for SAR quality parameter including total dissolved salts, sodium, bicarbonate, and for EC quality parameter including sulfate, calcium, magnesium and ratio Sodium uptake from 2010 to 2017. The results indicated that the highest prediction accuracy of Quality Parameters of EC and TDS is related to the ANN + PSO model with the tangent sigmoid activation function and for the SAR parameter is related to the ANN + PSO model with the logarithm sigmoid activation function so that the MAE and RMSE statistics had the minimum and R2 had the maximum value for the model. In the test phase the values calculated were for EC parameter RMSE=14. 61, MAE=9. 27, NRMSE=0. 41, EF=0. 942, and R2=0. 96 and for TDS parameter RMSE=22. 21, MAE=18. 32, NRMSE=0. 398, EF=0. 925, and R2=0. 836 and for SAR parameter RMSE=9. 45, MAE=7. 2, NRMSE=0. 301, EF=9. 27, and R2=0. 974. In addition, the results of the mean comparison between measured and simulated data showed that the predicted values with models were not significantly different with the measured date.

Cites

No record.

References

No record.

Cite

APA: Copy

Ahanin Jan, Kimia, & EGDERNEZHAD, ASLAN. (2020). Modeling Qualitative Parameters of SAR, EC, and TDS in Groundwater using Optimized Artificial Neural Network Model (Case Study: Behbahan Plain). JOURNAL OF ENVIRONMENT AND WATER ENGINEERING, 6(2 ), 161-172. SID. https://sid.ir/paper/360901/en

Vancouver: Copy

Ahanin Jan Kimia, EGDERNEZHAD ASLAN. Modeling Qualitative Parameters of SAR, EC, and TDS in Groundwater using Optimized Artificial Neural Network Model (Case Study: Behbahan Plain). JOURNAL OF ENVIRONMENT AND WATER ENGINEERING[Internet]. 2020;6(2 ):161-172. Available from: https://sid.ir/paper/360901/en

IEEE: Copy

Kimia Ahanin Jan, and ASLAN EGDERNEZHAD, “Modeling Qualitative Parameters of SAR, EC, and TDS in Groundwater using Optimized Artificial Neural Network Model (Case Study: Behbahan Plain),” JOURNAL OF ENVIRONMENT AND WATER ENGINEERING, vol. 6, no. 2 , pp. 161–172, 2020, [Online]. Available: https://sid.ir/paper/360901/en

Related Journal Papers