Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer

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.

NOROUZI HOSSEIN; NADIRI ATA ALLAH; ASGHARI MOGHADDAM ASGHAR; NOROUZI MARYAM

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

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF ECOHYDROLOGY Year:2018 | Volume:5 | Issue:3 Page(s): 739-751

Download Full-Text

Persian Verion

View:

798

Download:

Cites:

Information Journal Paper

Title

Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer

Author(s)

NOROUZI HOSSEIN | NADIRI ATA ALLAH | ASGHARI MOGHADDAM ASGHAR | NOROUZI MARYAM | Issue Writer Certificate

Keywords

Artificial IntelligenceQ2

GroundwaterQ1

Malekan PlainQ2

Random ForestQ2

TransmissivityQ2

Abstract

Estimating aquifer hydrogeological parameters is essential for the studies or management of Groundwater resources. There are several different methods such as pumping test, simulation or modeling of Groundwater, geophisical modeling to estimate these parameters. Although analysis and evaluation of pumping test data is the best way to achieve this purpose, it is costly, time consuming and the gained results are from limited points. Malekan Plain aquifer is one of the marginal plains of Urmia Lake which suffered more ground water declination and Salinization in last decades and it needs qualitative and quantitative management. In this study, artificial neural networks, fuzzy logic and Random Forest models have been used to estimate the transmission of aquifers and the performance each of these models has been investigated. Inputs of presented models included related geophysical and hydrogeological variables to Transmissivity such as transverse resistivity (Rt), electric conductivity (EC), alluvium thickness (B), and hydraulic conductivity (k). Based on the results of all models, Random Forest model has higher accuracy and ability to predict Transmissivity parameter. According to this model, electrical conductivity (EC), aquifer environment (A) and hydraulic gradient (H) parameters were identified as the most important parameters to predict the Transmissivity, respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

NOROUZI, HOSSEIN, NADIRI, ATA ALLAH, ASGHARI MOGHADDAM, ASGHAR, & NOROUZI, MARYAM. (2018). Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer. IRANIAN JOURNAL OF ECOHYDROLOGY, 5(3 ), 739-751. SID. https://sid.ir/paper/254002/en

Vancouver: Copy

NOROUZI HOSSEIN, NADIRI ATA ALLAH, ASGHARI MOGHADDAM ASGHAR, NOROUZI MARYAM. Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer. IRANIAN JOURNAL OF ECOHYDROLOGY[Internet]. 2018;5(3 ):739-751. Available from: https://sid.ir/paper/254002/en

IEEE: Copy

HOSSEIN NOROUZI, ATA ALLAH NADIRI, ASGHAR ASGHARI MOGHADDAM, and MARYAM NOROUZI, “Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer,” IRANIAN JOURNAL OF ECOHYDROLOGY, vol. 5, no. 3 , pp. 739–751, 2018, [Online]. Available: https://sid.ir/paper/254002/en

Related Journal Papers