Estimating the parameters of Philip infiltration equation 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.

Abrishami Shirazi N.; Sepaskhah A. R.

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

Journal Paper

Paper Information

Journal: Iran Agricultural Research Year:2019 | Volume:38 | Issue:2 Page(s): 25-36

Download Full-Text

Persian Verion

View:

404

Download:

Cites:

Information Journal Paper

Title

Estimating the parameters of Philip infiltration equation using artificial neural network

Author(s)

Abrishami Shirazi N. | Sepaskhah A. R. | Issue Writer Certificate

Keywords

Artificial neural networksQ2

Hydraulic conductivityQ1

Philip equationQ3

Sorption coefficientQ3

Water infiltrationQ3

Abstract

Infiltration rate is one of the most important parameters used in irrigation water management. Direct measurement of infiltration process is laborious, time consuming and expensive. Therefore, in this study application of some indirect methods such as Artificial neural networks (ANNs) for prediction of this phenomenon was investigated. Different ANNs structures including two training algorithms (TrainLM and TrainBR), two transfer functions (Tansig and Logsig), and different combinations of the input variables such as sand, silt, and clay fractions, bulk density (BD), soil organic matter (SOM), cumulative infiltration (CI) and elapsed time were used to predict Sorption coefficient (S) and Hydraulic conductivity (A) in Philip equation (I=S*t0. 5+A*t), which corresponded to 30 soil samples from study areas located in the Agricultural College, Shiraz University, (Bajgah). A two-hidden layer ANNs with two and three neurons in the hidden layers, respectively and TrainLM algorithm performed the best in predicting S when Logsig and Tansig were used. Silt+ clay+ sand+ time+ CI combination was the most basic influential variables for the S prediction. Furthermore, a two-hidden layer ANNs with two and three neurons in the hidden layers, respectively and TrainBR algorithm performed the best in predicting A when Tansig and Tansig were used. Silt +clay +sand +BD + OM+ time+ CI combination was the most basic influential variables for A prediction. Results showed that increasing the hidden layers and input variables significantly improved the ANNs performance. The coefficient of determination (R2) confirmed that the ANNs predictions for A (84. 6 %) fit data better than S (77. 5 %).

Cites

No record.

References

No record.

Cite

APA: Copy

Abrishami Shirazi, N., & Sepaskhah, A. R.. (2019). Estimating the parameters of Philip infiltration equation using artificial neural network. IRAN AGRICULTURAL RESEARCH, 38(2 ), 25-36. SID. https://sid.ir/paper/361426/en

Vancouver: Copy

Abrishami Shirazi N., Sepaskhah A. R.. Estimating the parameters of Philip infiltration equation using artificial neural network. IRAN AGRICULTURAL RESEARCH[Internet]. 2019;38(2 ):25-36. Available from: https://sid.ir/paper/361426/en

IEEE: Copy

N. Abrishami Shirazi, and A. R. Sepaskhah, “Estimating the parameters of Philip infiltration equation using artificial neural network,” IRAN AGRICULTURAL RESEARCH, vol. 38, no. 2 , pp. 25–36, 2019, [Online]. Available: https://sid.ir/paper/361426/en

Related Journal Papers