Egypt faces a severe water scarcity in the last years. Increasing population cause rising in water demands and fast economic growth leads to ecosystem degradation. In addition, ineffective irrigation methods with water misuse result in water quality degradation. River Nile is the main fresh water source in Egypt. This study evaluates, one of River Nile branches; Rosetta water quality through Geographical Information System (GIS) techniques. Fifteen water samples were analyzed for their chemical and biological properties. A mathematical model of Water Quality Index (WQI) has been built to integrate biochemical data as input parameters. This model describes the spatial distribution. On the other hand, the temporal of water quality status has been defined. A spatial variation of water quality index was generalized for the study area. The average water quality index values range between 58.8 and 67.2. Generally, the water quality index values within the study area were about the critical pollution level. The concentrations of most elements in the studied water samples were above the permissible levels for drinking water standards. This study concludes that Rosetta water is not suitable for drinking. Furthermore, it can be used for irrigation and domestic uses with specified treatments.

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRC

Groundwater is the main source of water supply in Jordan. Due to lower precipitation rates in recent years, the surface water is increasingly limited. Research on groundwater vulnerability helps protect this main source of water supply and assists in the development of plans to confront the deterioration and contamination of aquifers from wastewater and agricultural activities in order to protect groundwater for future generations. The important factors to determine groundwater vulnerability are environmental conditions; hence the spatial conditions in arid to semi-arid areas must be taken into account when applying different models. The aquifer vulnerability has been assessed in Jordan by using the DRASTIC method; remote sensing and Geographic Information System (GIS) data were used to derive and process data. The DRASTIC index was used with seven parameters to describe physical characteristics of the aquifers. It is concluded that about 34% of the area was considered to be of moderate vulnerability, but the share increased to 60% after modifying the index. While high vulnerability was at 25% of the total area, it decreased to 6.3% with the modified index; therefore, urgent pollution prevention measures should be taken for every kind of relevant activity within the whole basin.

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Land Surface Temperature (LST) is one of the important indicators to understand the spatial changes and surface processes on the earth surface that leads to actual assessment of environmental quality from local to global scales. The relation between spatial analysis of the land surface temperature and existing land use/land cover changes is important to evaluate the climate processes. Monitoring of this relation in the arid and semi-arid regions is necessary to make an appropriate decision about Land surface temperature and environmental status. In this paper, generally the split-window algorithm is used to estimate LST from thermal bands of the Landsat Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) using remote sensing and Geographic Information System (GIS) techniques as well as meteorological data through Moderate Resolution Imaging Spectroradiometer (MODIS). The results show the relationships between land use types and land surface temperature. MODIS data were analyzed. The relationship between MODIS and Landsat data temperature is moderate relation and the (R2 = 0.5109) according on 200 random points were selected. This research concludes that the maximum temperatures of the land use types in MODIS and Landsat data for the rock formation are 59˚ and 45˚ respectively, whereas the maximum temperatures of the geological formation in MODIS and Landsat data for the basalt are 59˚ and 45˚ respectively. In conclusion, the MODIS and Landsat OLI and TIRS Data have high ability to distinguish the land use types. The correlation coefficient of the relation between the surface temperature with rock was (R2 = 0.6197). Therefore, it is found that there is an ability to monitor the How to cite this paper: Ibrahim, M. and Abu-Mallouh, H.