SIMULATION OF GROUND WATER SALINITY BY USING ARTIFICIAL NEURAL NETWORK (ANN) ON THE MAZANDARAN PROVINCE COASTS

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.

DERAKHSHAN SH.; GHOLAMI V.; DARVARI Z.

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

Journal Paper

Paper Information

Journal: Journal of Irrigation Sciences and Engineering Year:2013 | Volume:36 | Issue:2 Page(s): 61-70

Download Full-Text

Persian Verion

View:

1,084

Download:

Cites:

Information Journal Paper

Title

SIMULATION OF GROUND WATER SALINITY BY USING ARTIFICIAL NEURAL NETWORK (ANN) ON THE MAZANDARAN PROVINCE COASTS

Author(s)

DERAKHSHAN SH. | GHOLAMI V. | DARVARI Z. | Issue Writer Certificate

Keywords

GROUND WATER SALINITYQ2

ANNQ2

MAZANDARANQ3

Abstract

Groundwater is one of the most important water resources and its qualitative study is very important for water resources protection and planning. Also, qualitative parameters measuring of the ground water is costly and time consumer. Models will be reduced the cost of water qualitative estimations and it will be provided a data bank to manage water resources. In this study, Artificial Neural Network (ANN) has been used to simulate GROUND WATER SALINITY on the Caspian southern coasts (Mazandaran Province). So, a comprehensive data bank of water qualitative experiments has been provided and then quantitative values of the effective factors on GROUND WATER SALINITY, such as: Water conductivity of aquifer formation, surface water salinity, slope and elevation (topography) and the distance from Caspian Sea were estimated. The efficiency of Artificial Neural Network (ANN) has been considered through two parameters: Median Root of Square of the Error (RMSE) and co efficiency between the actual and desirable outputs (R). The results showed that co efficiency between the actual and desirable outputs (R) is 0.75 in the best network structure. surface water salinity, water conductivity of aquifer formation and distance from Sea are the best inputs for simulating GROUND WATER SALINITY and those are the most important factors on GROUND WATER SALINITY in MAZANDARAN Province Coasts.

Cites

No record.

References

No record.

Cite

APA: Copy

DERAKHSHAN, SH., GHOLAMI, V., & DARVARI, Z.. (2013). SIMULATION OF GROUND WATER SALINITY BY USING ARTIFICIAL NEURAL NETWORK (ANN) ON THE MAZANDARAN PROVINCE COASTS. IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), 36(2), 61-70. SID. https://sid.ir/paper/216956/en

Vancouver: Copy

DERAKHSHAN SH., GHOLAMI V., DARVARI Z.. SIMULATION OF GROUND WATER SALINITY BY USING ARTIFICIAL NEURAL NETWORK (ANN) ON THE MAZANDARAN PROVINCE COASTS. IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE)[Internet]. 2013;36(2):61-70. Available from: https://sid.ir/paper/216956/en

IEEE: Copy

SH. DERAKHSHAN, V. GHOLAMI, and Z. DARVARI, “SIMULATION OF GROUND WATER SALINITY BY USING ARTIFICIAL NEURAL NETWORK (ANN) ON THE MAZANDARAN PROVINCE COASTS,” IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), vol. 36, no. 2, pp. 61–70, 2013, [Online]. Available: https://sid.ir/paper/216956/en

Related Journal Papers