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Your search keyword '"BRADAÏ, Abdelhamid"' showing total 12 results
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12 results on '"BRADAÏ, Abdelhamid"'

3. Evaporation Estimation Using Landsat 8 Data and Its Contribution to Salts Dynamics in the Lower Cheliff Plain (Algeria)

Author

Yahiaoui, Ibrahim, Douaoui, Abdelkader, Bradaï, Abdelhamid, Khaldi, Abdelkrim, Mokhtari, Fella, Abdalla, Hassan, Series editor, Abdul Mannan, Md., Series editor, Alalouch, Chaham, Series editor, Attia, Sahar, Series editor, Boemi, Sofia Natalia, Series editor, Bougdah, Hocine, Series editor, Bozonnet, Emmanuel, Series editor, De Bonis, Luciano, Series editor, Hawkes, Dean, Series editor, Kostopoulou, Stella, Series editor, Mahgoub, Yasser, Series editor, Mesbah Elkaffas, Saleh, Series editor, Mohareb, Nabil, Series editor, O. Gawad, Iman, Series editor, Oostra, Mieke, Series editor, Pignatta, Gloria, Series editor, Pisello, Anna Laura, Series editor, Rosso, Federica, Series editor, Kallel, Amjad, editor, Ksibi, Mohamed, editor, Ben Dhia, Hamed, editor, and Khélifi, Nabil, editor

Published
2018
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4. Applied of Non-parametric Kriging Method to Improve Soil Salinity Map: The Case Study of the Lower Cheliff Plain, Algeria

Author

Koulla, Noureddine, Bradaï, Abdelhamid, Douaoui, Abdelkader, Abdalla, Hassan, Series editor, Abdul Mannan, Md., Series editor, Alalouch, Chaham, Series editor, Attia, Sahar, Series editor, Boemi, Sofia Natalia, Series editor, Bougdah, Hocine, Series editor, Bozonnet, Emmanuel, Series editor, De Bonis, Luciano, Series editor, Hawkes, Dean, Series editor, Kostopoulou, Stella, Series editor, Mahgoub, Yasser, Series editor, Mesbah Elkaffas, Saleh, Series editor, Mohareb, Nabil, Series editor, O. Gawad, Iman, Series editor, Oostra, Mieke, Series editor, Pignatta, Gloria, Series editor, Pisello, Anna Laura, Series editor, Rosso, Federica, Series editor, Kallel, Amjad, editor, Ksibi, Mohamed, editor, Ben Dhia, Hamed, editor, and Khélifi, Nabil, editor

Published
2018
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5. Geochemical Characteristics of Groundwater and Its Suitability for Irrigation in the Lower Cheliff Plain, Algeria

Author

Bradaï, Abdelhamid, Douaoui, Abdelkader, Yahiaoui, Ibrahim, Abdalla, Hassan, Series editor, Abdul Mannan, Md., Series editor, Alalouch, Chaham, Series editor, Attia, Sahar, Series editor, Boemi, Sofia Natalia, Series editor, Bougdah, Hocine, Series editor, Bozonnet, Emmanuel, Series editor, De Bonis, Luciano, Series editor, Hawkes, Dean, Series editor, Kostopoulou, Stella, Series editor, Mahgoub, Yasser, Series editor, Mesbah Elkaffas, Saleh, Series editor, Mohareb, Nabil, Series editor, O. Gawad, Iman, Series editor, Oostra, Mieke, Series editor, Pignatta, Gloria, Series editor, Pisello, Anna Laura, Series editor, Rosso, Federica, Series editor, Kallel, Amjad, editor, Ksibi, Mohamed, editor, Ben Dhia, Hamed, editor, and Khélifi, Nabil, editor

Published
2018
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8. Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods in the Lower Cheliff plain, Algeria

Author

Bradaï Abdelhamid, Koulla Noureddine, Celso Augusto Guimarães Santos, Victor Nascimento, Achite Mohammed, and Douaoui Abdelkader

Subjects
Urban Studies, Environmental Engineering, Soil salinity, Artificial neural network, Kriging, Linear regression, Soil science, Mathematics
Abstract

Soil salinity is one of the most damaging environmental issues worldwide, essentially in arid and semi-arid regions, caused by various factors. Spatial estimation and prediction of salinity is important to predict land evaluation in order to develop and determine leaching factor and the precise management for maximum production. The Lower Cheliff is characterized by the augmentation of rate of soil salinity with 80 % of area. In this study, we have analyzed the relationship between both elevation and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. To conduct this work, we have taken 406 samples and analysis of electric conductivity as well as measurement of the elevation with a GPS, we analyzed the correlations of soil salinity with elevation. In this study we have given a great focus on the use of the multiple linear regressions, Ordinary kriging and artificial neural network methods, the results showed that soil salinity had a good correlation with elevation, and according to the values of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE), implied superiority of MLP model with the value of R² = 0.994, RMSE= 0.63 and MAE = 0.33.

Published
2019

11. Performance of random forest and buffer analysis of Sentinel-2 data for modelling soil salinity in the Lower-Cheliff plain (Algeria).

Author

Yahiaoui, Ibrahim, Bradaï, Abdelhamid, Douaoui, Abdelkader, and Abdennour, Mohamed Amine

Subjects
*SOIL salinity, *RANDOM forest algorithms, *DATA analysis, *DATA modeling, *FORECASTING, *SOIL sampling
Abstract

Modelling approaches are becoming an efficient tool in the forecasting of the salinization spread impact on the local and global scales. In the Lower-Cheliff plain, updating the information on soil salinity expansion is more than required as it continues to damage the agricultural environment in there. Through this study, we adopted an artificial methodology that consists of a combination of buffer analysis using Sentinel-2 data and machine learning modelling to assess their aptitude in the prediction and mapping of soil salinity. The adopted random forest (RF) algorithm included the reflectance information from of the bands from Blue ( ρ B l u e ), Green ( ρ G r e e n ), Red ( ρ R e d ), Near infrared (NIR), Vegetation red-edge (VRE) and Shortwave Infrared (SWIR), optimized with the geospatial buffering based on the 91 soil random samples collected during the summer of 2019 and measured for the Electrical Conductivity (EC) in the laboratory. The outputs from the geospatial buffering refined the goodness of the correlation between field data and the variables set from bands reflectance and selected salinity indices. The obtained coefficient of determination (R2) with Multiple Linear Regression (MLR) and Partial Least Square regression (PLS) models proved an improvement in the multivariable prediction of soil salinity with the optimized Sentinel-2 data (R2 = 0.61 and 0.68 + RMSE of 3.36 and 2.87 dS m−1 respectively), compared to the results from the 2015 multiple regression model, Given the high values of R2 = 0.77 and 0.95 and the low values of RMSE = 2.29 and 1.18 dS m−1 respectively; the RF regressor was very efficient in predicting EC either with all variables or with selected variables from the variable importance measure based on the mean decrease accuracy (MDA). The random forest classifier performed top of the range classification with an overall Accuracy (OA) about 99%, estimating that over 75% of the study area surface is suffering from the salinization extent. [ABSTRACT FROM AUTHOR]

Published
2021
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