Studying and determining the physical properties and hydraulic parameters of vadose zone sediment... more Studying and determining the physical properties and hydraulic parameters of vadose zone sediments is an important key to evaluate the infiltration rate into them and assessing the extent to which aquifer sediments benefit from rainwater harvesting in arid and semi-arid areas. Due to the lack of sufficient data on the characteristics of this zone depths, a numerical modeling was used to simulate the electrical resistivity of these sediments by applying the electrical resistivity method, because it is the most affected by the physical properties of dry and wet sediments. This study was applied as a proposal for application in northwestern KSA to calculate the vertical hydraulic conductivity and transmissivity for the vadose zone. This was implemented by assuming a three-layer model using COMSOL Multiphysics model with different electrical resistivity values depending on some in situ electrical resistivity measurements for shallow depths. Hence, the infiltration rate of sediments in t...
Study region: A pilot case study in East El Oweinat (PCSEO), Egypt. Study focus: An artificial ne... more Study region: A pilot case study in East El Oweinat (PCSEO), Egypt. Study focus: An artificial neural network (ANN)-based mountain gazelle optimization (MGO) model was applied to map groundwater potential zones (GWPZs). For this purpose, ten layers affecting groundwater occurrence were prepared and normalized against the groundwater drawdown (DD) map. All data was divided into 70:30 for training and testing. After that, sensitivity analysis was adopted to verify the relative importance (RI) of layers. The accuracy of GWPZs was checked using the receiver operating characteristic (ROC) curve and other statistical indicators. The model was finally applied to propose a sustainable strategy for groundwater exploration by implementing the integrated MODFLOW-USG and MGO framework. New hydrological insights for the region: Over 40% of the PCSEO revealed high to very high groundwater potential degrees and was situated mostly on the southwestern side. Sensitivity analysis revealed that GWPZs were significantly affected by groundwater table (GWT), well density (WD), and land use (LU). The results also indicated that the ANN-based MGO model performed well with an area under curve (AUC) of ~ 90% compared to other conventional models. Additionally, the MODFLOW-USG-based MGO model gave a spatial distribution of optimal discharge and well-depth zones. This finding could significantly match SDGs relevant to ending poverty, affordable groundwater, and life on land.
Sustainable development in any region depends on authoritative monitoring of land cover change in... more Sustainable development in any region depends on authoritative monitoring of land cover change information. In this study, a hybrid classification methodology and post-classification change detection techniques were applied to five temporal data sets of images Landsat images acquired in 1984, 1990, 1998, 2006 and 2015, respectively, to map and monitor land cover changes in patterns related to agricultural development, water resources management and urban expansion in the desert fringes of the Eastern Nile Delta region of Egypt. Each temporal data set consists of two Landsat scenes, which were mosaicked to cover the whole region. Four different supervised classification algorithms were implemented to produce five land use maps. The maximum likelihood method showed higher classification accuracies in comparison with other methods. Postclassification comparison of these maps was used to obtain ‘from–to’ change detection maps. The results show that the rapid increase changes occurred am...
Sustainable development in any region depends on authoritative monitoring of land cover change in... more Sustainable development in any region depends on authoritative monitoring of land cover change information. In this study, a hybrid classification methodology and post-classification change detection techniques were applied to five temporal data sets of images Landsat images acquired in 1984, 1990, 1998, 2006 and 2015, respectively, to map and monitor land cover changes in patterns related to agricultural development, water resources management and urban expansion in the desert fringes of the Eastern Nile Delta region of Egypt. Each temporal data set consists of two Landsat scenes, which were mosaicked to cover the whole region. Four different supervised classification algorithms were implemented to produce five land use maps. The maximum likelihood method showed higher classification accuracies in comparison with other methods. Post-classification comparison of these maps was used to obtain 'from–to' change detection maps. The results show that the rapid increase changes occurred among two land cover classes (urban, and agricultural land) led to shortages in the desert land area because land reclamation and the construction of new cities in the desert. Increase in urbanization led to the agricultural land degradation due to urban encroachment at the fringes of urban centers. Slight land cover changes were detected between marshland and surface water land cover classes.
Studying and determining the physical properties and hydraulic parameters of vadose zone sediment... more Studying and determining the physical properties and hydraulic parameters of vadose zone sediments is an important key to evaluate the infiltration rate into them and assessing the extent to which aquifer sediments benefit from rainwater harvesting in arid and semi-arid areas. Due to the lack of sufficient data on the characteristics of this zone depths, a numerical modeling was used to simulate the electrical resistivity of these sediments by applying the electrical resistivity method, because it is the most affected by the physical properties of dry and wet sediments. This study was applied as a proposal for application in northwestern KSA to calculate the vertical hydraulic conductivity and transmissivity for the vadose zone. This was implemented by assuming a three-layer model using COMSOL Multiphysics model with different electrical resistivity values depending on some in situ electrical resistivity measurements for shallow depths. Hence, the infiltration rate of sediments in t...
Study region: A pilot case study in East El Oweinat (PCSEO), Egypt. Study focus: An artificial ne... more Study region: A pilot case study in East El Oweinat (PCSEO), Egypt. Study focus: An artificial neural network (ANN)-based mountain gazelle optimization (MGO) model was applied to map groundwater potential zones (GWPZs). For this purpose, ten layers affecting groundwater occurrence were prepared and normalized against the groundwater drawdown (DD) map. All data was divided into 70:30 for training and testing. After that, sensitivity analysis was adopted to verify the relative importance (RI) of layers. The accuracy of GWPZs was checked using the receiver operating characteristic (ROC) curve and other statistical indicators. The model was finally applied to propose a sustainable strategy for groundwater exploration by implementing the integrated MODFLOW-USG and MGO framework. New hydrological insights for the region: Over 40% of the PCSEO revealed high to very high groundwater potential degrees and was situated mostly on the southwestern side. Sensitivity analysis revealed that GWPZs were significantly affected by groundwater table (GWT), well density (WD), and land use (LU). The results also indicated that the ANN-based MGO model performed well with an area under curve (AUC) of ~ 90% compared to other conventional models. Additionally, the MODFLOW-USG-based MGO model gave a spatial distribution of optimal discharge and well-depth zones. This finding could significantly match SDGs relevant to ending poverty, affordable groundwater, and life on land.
Sustainable development in any region depends on authoritative monitoring of land cover change in... more Sustainable development in any region depends on authoritative monitoring of land cover change information. In this study, a hybrid classification methodology and post-classification change detection techniques were applied to five temporal data sets of images Landsat images acquired in 1984, 1990, 1998, 2006 and 2015, respectively, to map and monitor land cover changes in patterns related to agricultural development, water resources management and urban expansion in the desert fringes of the Eastern Nile Delta region of Egypt. Each temporal data set consists of two Landsat scenes, which were mosaicked to cover the whole region. Four different supervised classification algorithms were implemented to produce five land use maps. The maximum likelihood method showed higher classification accuracies in comparison with other methods. Postclassification comparison of these maps was used to obtain ‘from–to’ change detection maps. The results show that the rapid increase changes occurred am...
Sustainable development in any region depends on authoritative monitoring of land cover change in... more Sustainable development in any region depends on authoritative monitoring of land cover change information. In this study, a hybrid classification methodology and post-classification change detection techniques were applied to five temporal data sets of images Landsat images acquired in 1984, 1990, 1998, 2006 and 2015, respectively, to map and monitor land cover changes in patterns related to agricultural development, water resources management and urban expansion in the desert fringes of the Eastern Nile Delta region of Egypt. Each temporal data set consists of two Landsat scenes, which were mosaicked to cover the whole region. Four different supervised classification algorithms were implemented to produce five land use maps. The maximum likelihood method showed higher classification accuracies in comparison with other methods. Post-classification comparison of these maps was used to obtain 'from–to' change detection maps. The results show that the rapid increase changes occurred among two land cover classes (urban, and agricultural land) led to shortages in the desert land area because land reclamation and the construction of new cities in the desert. Increase in urbanization led to the agricultural land degradation due to urban encroachment at the fringes of urban centers. Slight land cover changes were detected between marshland and surface water land cover classes.
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