Investigating the effect of Wetness index and spectral data on the estimation of soil particles percentage using neuro-fuzzy, artificial neural network and regression tree models

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.

Mehrabi Gohari elham; MATINFAR HAMID REZA; TAGHIZADEH MEHRJARDI ROHOLLAH; JAFARI AZAM

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:2020 | Volume:10 | Issue:38 Page(s): 104-123

Download Full-Text

Persian Verion

View:

454

Download:

Cites:

Information Journal Paper

Title

Investigating the effect of Wetness index and spectral data on the estimation of soil particles percentage using neuro-fuzzy, artificial neural network and regression tree models

Author(s)

Mehrabi Gohari elham | MATINFAR HAMID REZA | TAGHIZADEH MEHRJARDI ROHOLLAH | JAFARI AZAM | Issue Writer Certificate

Keywords

Wetness IndexQ1

VIS_NIRQ1

Artificial Neural NetworkQ2

Tree RegressionQ1

Neuro-FuzzyQ1

Abstract

Direct estimation of some soil characteristics is time consuming, costly and sometimes not possible. In recent years, indirect methods have been used to estimate these properties. In the present study, to predict the soil texture fractions, 115 profiles were identified based on the Hypercube technique, and the horizons were sampled and the percentage of sand, clay and silt of soil samples were measured. Environmental variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial Neural Network, regression tree and Neuro-Fuzzy models were used to make a correlation between soil data (clay, sand and silt) and environmental variables. The results of this study showed that the Neuro-Fuzzy model was more accurate in prediction of the three parameters of clay, sand and silt than Artificial Neural Network and Tree Regression. The RMSE value in the neuro fuzzy model was compared to regression tree model. The neuro fuzzy model results were, for clay surface 1. 43 %, for sand surface 1. 98% and for silt surface 2. 1% that reduced by 6. 71%, 8. 49% and 5. 42% for clay, sand and silt respectively, compared to regression tree model. The results also showed that the most important auxiliary variables are spectrometric data followed by MrVBF and Wetness Index.

Cites

No record.

References

No record.

Cite

APA: Copy

Mehrabi Gohari, elham, MATINFAR, HAMID REZA, TAGHIZADEH MEHRJARDI, ROHOLLAH, & JAFARI, AZAM. (2020). Investigating the effect of Wetness index and spectral data on the estimation of soil particles percentage using neuro-fuzzy, artificial neural network and regression tree models. IRANIAN OF IRRIGATION & WATER ENGINEERING, 10(38 ), 104-123. SID. https://sid.ir/paper/247179/en

Vancouver: Copy

Mehrabi Gohari elham, MATINFAR HAMID REZA, TAGHIZADEH MEHRJARDI ROHOLLAH, JAFARI AZAM. Investigating the effect of Wetness index and spectral data on the estimation of soil particles percentage using neuro-fuzzy, artificial neural network and regression tree models. IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2020;10(38 ):104-123. Available from: https://sid.ir/paper/247179/en

IEEE: Copy

elham Mehrabi Gohari, HAMID REZA MATINFAR, ROHOLLAH TAGHIZADEH MEHRJARDI, and AZAM JAFARI, “Investigating the effect of Wetness index and spectral data on the estimation of soil particles percentage using neuro-fuzzy, artificial neural network and regression tree models,” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 10, no. 38 , pp. 104–123, 2020, [Online]. Available: https://sid.ir/paper/247179/en

Related Journal Papers