Comparison of the performance of artificial neural networks and gene expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft 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.

JABALBAREZI B.; MALEKIAN A.

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

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH Year:2019 | Volume:26 | Issue:2 (75) Page(s): 292-301

Download Full-Text

Persian Verion

View:

501

Download:

Cites:

Information Journal Paper

Title

Comparison of the performance of artificial neural networks and gene expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain)

Author(s)

JABALBAREZI B. | MALEKIAN A. | Issue Writer Certificate

Keywords

Artificial neural networksQ2

groundwater tableQ2

gene expressionQ1

Jiroft plainQ1

Abstract

Modeling and prediction of groundwater level is one of the basic tasks to achieve optimal management of water resources. One way to predict the groundwater level is using artificial intelligence techniques such as neural networks and gene expression planning. The aim of this study was to evaluate the effectiveness of artificial neural network (ANN) and gene expression methods in predicting groundwater level of Jiroft plain aquifer. For this purpose, the data from 65 piezometric wells in the Jiroft plain aquifer was used for a period of eleven years. The level of piezometric wells by each of the techniques of gene expression and neural network were simulated separately and at the end, the root mean square, mean absolute error, and R2 were used to determine the accuracy of the predictions of each of the methods. The results of this study showed the higher efficiency and accuracy of both neural network techniques and gene expression in predicting the groundwater level region. The correlation coefficient in the artificial neural network method gene expression method was equal to 0. 96 and 0. 72, respectively, indicating the higher efficiency of artificial neural network in the simulation of Jiroft plain groundwater data over the period studied.

Cites

No record.

References

No record.

Cite

APA: Copy

JABALBAREZI, B., & MALEKIAN, A.. (2019). Comparison of the performance of artificial neural networks and gene expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain). IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH, 26(2 (75) ), 292-301. SID. https://sid.ir/paper/360610/en

Vancouver: Copy

JABALBAREZI B., MALEKIAN A.. Comparison of the performance of artificial neural networks and gene expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain). IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH[Internet]. 2019;26(2 (75) ):292-301. Available from: https://sid.ir/paper/360610/en

IEEE: Copy

B. JABALBAREZI, and A. MALEKIAN, “Comparison of the performance of artificial neural networks and gene expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain),” IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH, vol. 26, no. 2 (75) , pp. 292–301, 2019, [Online]. Available: https://sid.ir/paper/360610/en

Related Journal Papers