Simulation of Nitrate Distribution Pattern in Drip Irrigation Systems using 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.

KARIMI BAKHTIAR; MOHAMMADI PARVA

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

Journal Paper

Paper Information

Journal: Journal of Irrigation and Water Engineering Year:2019 | Volume:10 | Issue:37 Page(s): 181-195

Download Full-Text

Persian Verion

View:

373

Download:

Cites:

Information Journal Paper

Title

Simulation of Nitrate Distribution Pattern in Drip Irrigation Systems using artificial neural network

Author(s)

KARIMI BAKHTIAR | MOHAMMADI PARVA | Issue Writer Certificate

Keywords

drip irrigationQ1

artificial neural networkQ2

simulationQ2

nitrateQ2

Abstract

Surplus application of nitrate can lead to contamination of groundwater resources. Accurate knowledge of nitrate distribution in the root zone is essential for the design and management of drip irrigation systems. In this study, artificial neural network (ANN) model is used based on strong pattern recognition technique that made reasonable relations between input and output parameters. In order to simulate the nitrate distribution pattern, the Experiments were set on three different soil textures for surface and subsurface drip irrigation systems. The drippers were installed at 3 different soil depths (i. e. 15, 30 and 45 cm). The emitter outflows were considered as 2. 4, 4 and 6 lit/hr. The fertigation treatments include treatments with nitrate concentrations of 125, 250 and 375 mg/liter. The effective variables include the initial nitrate of soil, nitrate concentrations in fertigation, initial soil moisture content, radial distance of points, applied water volume, saturated hydraulic conductivity, emitter discharge, emitter installation depth, soil bulk density, and the proportions of sand, silt and clay of soil. The mentioned variables were utilized for estimation of nitrate distribution pattern of surface and subsurface drip irrigation systems using ANN model. The comparisons results of simulated and measured values indicated that ANN models were capable methods for prediction of nitrate distribution. The values of correlation coefficient (R) were ranged 0. 9-0. 94 and 0. 8-0. 96 for surface and subsurface drip irrigation systems, respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

KARIMI, BAKHTIAR, & MOHAMMADI, PARVA. (2019). Simulation of Nitrate Distribution Pattern in Drip Irrigation Systems using artificial neural network. IRANIAN OF IRRIGATION & WATER ENGINEERING, 10(37 ), 181-195. SID. https://sid.ir/paper/247167/en

Vancouver: Copy

KARIMI BAKHTIAR, MOHAMMADI PARVA. Simulation of Nitrate Distribution Pattern in Drip Irrigation Systems using artificial neural network. IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2019;10(37 ):181-195. Available from: https://sid.ir/paper/247167/en

IEEE: Copy

BAKHTIAR KARIMI, and PARVA MOHAMMADI, “Simulation of Nitrate Distribution Pattern in Drip Irrigation Systems using artificial neural network,” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 10, no. 37 , pp. 181–195, 2019, [Online]. Available: https://sid.ir/paper/247167/en

Related Journal Papers