Mahmoud Shahabi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Mahmoud Shahabi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
Ali Asghar Jafarzadeh
Faculty of Agriculture, Department of Soil Science, University of Tabriz, Tabriz, Iran
1 shared publication- Khalil Valizadeh Kamran
Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran
1 shared publication Mohammad Ali Ghorbani
Faculty of Agriculture, Department of Water Engineering, University of Tabriz, Tabriz, Iran
1 shared publicationMohammad Reza Neyshabouri
Faculty of Agriculture, Department of Soil Science, University of Tabriz, Tabriz, Iran
1 shared publication
Latest Publications
- 9 June 2016
- Published by Taylor & Francis in Archives of Agronomy and Soil Science
- Vol. 63 (2) , 151-160
Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods
Salinity as an important property of soil plays a major role in reducing the fertility in the world. Accurate information about the spatial change of soil salinity is essential for sustainable soil management and utilization in agriculture lands. For this purpose, 150 soil samples were collected from Dashte-e-Tabriz Iran and tested and soil salinity was estimated by land surface parameters including elevation, aspect, length of slope, wetness index, slope and normalized difference vegetation index as basic parameters. In order to model and predict the salinity, ordinary kriging (OK), artificial neural networks (ANN) and multiple linear regressions (MLR) were used. Accuracy of models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Based on Pearson correlation, elevation, normalized difference vegetation and wetness indices were selected for soil salinity spatial modeling from six land surface parameters. The results showed that the ANN had the lowest RMSE and highest R2. The values of R2, RMSE and MAE were 0.36, 25.89 and 17.06 for regression and 0.56, 17.70 and 13.05 for OK and 0.69, 16.06 and 11.60 for ANN, respectively, which indicated more accuracy of ANN in comparison with MLR and OK.
- 9 June 2016
- Published by Taylor & Francis in Archives of Agronomy and Soil Science
- Vol. 63 (2) , 151-160
Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods
Salinity as an important property of soil plays a major role in reducing the fertility in the world. Accurate information about the spatial change of soil salinity is essential for sustainable soil management and utilization in agriculture lands. For this purpose, 150 soil samples were collected from Dashte-e-Tabriz Iran and tested and soil salinity was estimated by land surface parameters including elevation, aspect, length of slope, wetness index, slope and normalized difference vegetation index as basic parameters. In order to model and predict the salinity, ordinary kriging (OK), artificial neural networks (ANN) and multiple linear regressions (MLR) were used. Accuracy of models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Based on Pearson correlation, elevation, normalized difference vegetation and wetness indices were selected for soil salinity spatial modeling from six land surface parameters. The results showed that the ANN had the lowest RMSE and highest R2. The values of R2, RMSE and MAE were 0.36, 25.89 and 17.06 for regression and 0.56, 17.70 and 13.05 for OK and 0.69, 16.06 and 11.60 for ANN, respectively, which indicated more accuracy of ANN in comparison with MLR and OK.