Thirty-three samples of groundwater were taken from Dibdibba unconfined aquifer in the Zuber area southwestern parts of Basrah governorate south of Iraq to assess the groundwater quality. A statistical multivariate analysis was done using cations and anions, pH, total dissolved solids (TDS), and electrical conductivity (EC) that were measured for drinking, livestock, and construction purposes. Residual sodium bicarbonate (RSBC), Magnesium Ratio (MR), and Permeability index (PI) were used to evaluate the suitability of the present samples for irrigation activity. The quality of groundwater in the study area is unsuitable for drinking water, industrial and building uses. But it is suitable for livestock uses, According to Residual Sodium Carbonate and Magnesium Ratio the groundwater in the study area are suitable for irrigation purposes, but unsuitable for Permeability index. Multivariate analysis results indicate the high positive correlation between Ec and TDS with other constituents, two significant clusters I and II are obtained with significant Ec and TDS responsible for playing the most effective in classifying the present samples. 71.85% and l2.21% of the present of the total variance of the groundwater samples were explained by Factor analyses, Factor I indicated increasing Cl-, Mg+2, Na+, and Ca+2 with the highest weight and Factor II show lower weight average of K+ concentration only. The results confirm the dissolution of sulfate salts and evaporate minerals, in addition to high agricultural lands and farm activities, besides the wastes from chemical construction industries.
Al-Najaf Al-Ashraf city located, in the southern part of Iraq, is located between latitudes (29ᵒ 50 00̋ -32ᵒ 21 00) north and longitudes (42 ° 50 '00 "-45 ° 44' 00") east, The rocks exposures at the study area date back to the (Middle Miocene-Recent) and this includes Dammam, Euphrates, Fatha, Injana, Zahra and Dubduba formations as well as Quaternary deposits.Depending on the climatic data recorded in Najaf station for the period show that the total falling rain was (97.54) mm, and the average temperature (25.75 C°), relative humidity (41.52%), wind speed (1.67 m / s), sunshine (8.6 hours / day) and the total evaporation from free surfaces was (3473.46 mm). The prevailing climate in the region is dry to semi-dry. was calculated corrected potential evapotranspiration theoretically apply Thornthwaite method where she was value 1024.97 mm, was also calculate the water surplus , which accounted for 22.1% divided into surface runoff and and Groundwater Recharge while the water deficit represents 77.9% of the annual rainfall.
Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.
The Rutba area is located within the arid region in the western desert of Iraq. Although no surface water is available in this area, the groundwater is considered the water source in the area. The aim of research is to investigate groundwater type and quality in the Rutba area using multivariate statistics approach. To evaluate the groundwater, fifteen groundwater samples were collected in the study region during the period of September 2019 and analyzed for essential anions and cations. The groundwater samples are considered to be fresh. Groundwater within the study region is dominated by calcium and sulphate ions refers to carbonate rock from the Mulussa Formation. The type of groundwater sample of the study region is earth alkaline water dominated by sulphate and chlorine and normal earth alkaline water with prevailing sulphate or chlorine except well no 3 which has been classified as alkalin water dominated by sulphate and chlorine. The correlation coefficient analyses of the groundwater samples show a strong correlation between Ca- Cl and Mg-Cl. The cluster Analysis is divided into four cluster of similar characteristics related to water quality. Principle components analysis shows high positive correlation between groundwater parameters and Factor 1. The Factor 1 reflects the role of the geogenic process like the dissolution of carbonate and dolomitic rocks prevalent in the study area.
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