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چکیده
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The Sulaymaniyah-Sharazoor basin in Iraqi Kurdistan Region is regarded as the most productive and has a large groundwater resource, often used for agriculture, irrigation, and drinking. This basin is located in the northeastern part of Iraq and comprises a large area of the eastern part of the Sulaymaniyah Governorate. The area is a mountain-encircled basin on Iraq's side of the Zagros Mountain belt. Geographically, it is located between the latitudes N35⁰ 35′ 40.15″ and N35⁰ 11′ 13″ and the longitudes E45⁰ 32′ 24.70″ and E 46⁰ 11′ 15.2′′. The basin coverage area is about 2503 km2 , and the topographical elevation ranges from 494 to 1533 meters above sea level. Except for Sulaymaniyah and Halabja municipal centers, the interest area includes six other subdistricts such as Arbat, Said Sadiq, Warmawa, Khormal, Byara, and Sirwan.The climate in Sulaimaniyah Governorate is typified by dry and hot weather in the summer and rainy, cold winter with temperature ranging from 6.8 °C to 33.6 °C. The average annual rainfall for the period (2000– 2022) is 656.5 mm, the average annual relative humidity is 49.9%, the average temperature is 24.15 °C, the average annual wind speed is 1.7 m/sec, and the annual pan evaporation is 177.6 mm. Water samples are collected from 27 deep wells, 6 shallow wells, and 7 springs for chemical analyses, especially for major cations and anions. The results show that all groundwater samples are suitable for different purposes according to Iraqi and World Health Organization standards and are characterized by a low dissolved solid content. The predominant ions are calcium and bicarbonate, and the chemical type of water is calcium bicarbonate. The majority of the water samples are suitable for different purposes. The present study has applied an Artificial Neural Network (ANN) and Gene Expression Programming (GEP) tool to predict the water quality parameters and spatial distribution of Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Total Hardness (TH) of groundwater for the study area. The physicochemical parameters of 221 wells were used for this computation. Many scenarios are considered by applying linear regression methods. The methods have been evaluated depending on the Correlation Coefficient (R) and Root Mean Square Error (RMSE) for the input combination, including TDS, Na⁺, Hardness, HCO₃⁻, K⁺, and NO₃⁻.The best scenarios for TH are M3 (ANN) and M2 (GEP) with RMSE values of 11.11 and 0.616, and for EC are T2 (ANN) and T7 (GEP) with RMSE values of 64.25 and 69.45, while for TDS they are N2 (ANN) and N6 (GEP) with RMSE values of 92.88 and 91.48. The comparison of the two models revealed that the GEP model will become more precise and reliable for the study area. The present study has applied GIS techniques for groundwater ii spatial distribution and hydrogeochemical modeling using NETPATHWin software for groundwater evaluation in the Sulaymaniyah-Sharazoor basin. Four flow paths were taken along the groundwater direction, and the output for the selected models revealed that the main hydrogeochemical reaction is dissolution-precipitation, and in some cases, there is cation exchange. Furthermore, the majority of water samples are undersaturated with respect to calcite, aragonite, dolomite, gypsum, anhydrite, and halite.
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