The modeling and prediction of the quality of the groundwater resources in Tuyserkan plain using the optimized artificial neural network

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.

Parsi Mehr Mohamad; Shayesteh Kamran; GODINI KAZEM

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

Journal Paper

Paper Information

Journal: Journal of Advances in Environmental Health Research Year:2020 | Volume:8 | Issue:2 Page(s): 107-118

Download Full-Text

View:

136

Download:

Cites:

Information Journal Paper

Title

The modeling and prediction of the quality of the groundwater resources in Tuyserkan plain using the optimized artificial neural network

Author(s)

Parsi Mehr Mohamad | Shayesteh Kamran | GODINI KAZEM | Issue Writer Certificate

Keywords

Artificial intelligence

Environmental assessment

Environmental pollution

Groundwater pollution

Abstract

Tuyserkan plain is an important agricultural plain located in Hamadan province, Iran. Despite the severe decline of the water levels in aquifers, the quality of the plain has not been evaluated in recent years. The present study aimed to analyze the data of 15 wells during 12 years to evaluate the quality of groundwater in this area using the Wilcox diagram for the aquifer. The electrical conductivity (EC) of the plain was interpolated using the Kriging method to evaluate its spatial distribution since this parameter has caused a decline in the quality of the groundwater in the plain. According to the findings, the EC value was higher in the eastern parts of the plain and Tuyserkan city, which was described as the spatial distribution of the parameter. The Pearson's correlation-coefficient was used to assess the correlations between EC and other parameters. To predict and model the EC value, multi-layer perceptron artificial neural network (MLP-ANN) were used. According to the results of the Pearson's correlation-coefficient, the reduced number of the data led to the decreased expenditures of the experiments in obtaining the input data. The third model was finally obtained with the lowest number of the input parameters, low error, and high correlations between the predicted and actual data. In this model, two input parameters and five neurons were obtained in a hidden layer (R: 0. 997, mean: 8. 634, NRMSE: 0. 05) using the momentum and hyperbolic tangent functions, indicating the high potential of MLP-ANNs in the prediction and modeling of groundwater quality. . .

Cites

No record.

References

Cite

APA: Copy

Parsi Mehr, Mohamad, Shayesteh, Kamran, & GODINI, KAZEM. (2020). The modeling and prediction of the quality of the groundwater resources in Tuyserkan plain using the optimized artificial neural network. JOURNAL OF ADVANCES IN ENVIRONMENTAL HEALTH RESEARCH, 8(2), 107-118. SID. https://sid.ir/paper/767377/en

Vancouver: Copy

Parsi Mehr Mohamad, Shayesteh Kamran, GODINI KAZEM. The modeling and prediction of the quality of the groundwater resources in Tuyserkan plain using the optimized artificial neural network. JOURNAL OF ADVANCES IN ENVIRONMENTAL HEALTH RESEARCH[Internet]. 2020;8(2):107-118. Available from: https://sid.ir/paper/767377/en

IEEE: Copy

Mohamad Parsi Mehr, Kamran Shayesteh, and KAZEM GODINI, “The modeling and prediction of the quality of the groundwater resources in Tuyserkan plain using the optimized artificial neural network,” JOURNAL OF ADVANCES IN ENVIRONMENTAL HEALTH RESEARCH, vol. 8, no. 2, pp. 107–118, 2020, [Online]. Available: https://sid.ir/paper/767377/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops