Investigation of surface and ground water resources of Ordu province in terms of water quality

This study was conducted to investigate the water quality of water resources in Ordu Province. Within the scope of the study, surface and groundwater analysis results in Ordu Province were evaluated according to Turkish Standards (TS 266), World Health Organization guidelines (WHO, 2006), and the Inland Surface Water Pollution Control Regulation. Additionally, the suitability of waters for drinking, domestic use, and irrigation was investigated. The quality indices of surface and groundwater were determined using the WAWQI and NSF-WQI methods. The relationships between the chemical parameters of the waters were identified through Spearman correlation test, principal component analysis, and cluster analysis. The aim was to develop a prediction model using artificial neural networks based on the water quality index values obtained from the NSF-WQI method.As a result of the evaluations, it was determined that in some surface water monitoring points and groundwater monitoring wells, the levels of chemical parameters exceeded the specified limit values in standards. The amount of ammonium in all surface waters exceeded the limit value defined in TS 266. According to hydrochemical classification, surface and groundwater were identified as Ca-Mg-HCO3 and Ca-HCO3 types. Strong positive correlations were found between HCO3-Mg (r=0.76) and Ca-HCO3 (r=0.84) according to Spearman correlation analysis. Based on SAR, magnesium hazard, RSC, and %Na parameters, waters from all surface water stations can be used for irrigation, while some groundwater is unsuitable for irrigation. According to the NSF-WQI water quality index, surface waters range between "moderate quality" and "good quality." According to the WAWQI water quality index, surface waters vary between "excellent," "good," and "not suitable for drinking" water quality, while groundwater varies between "excellent," "moderate," and "poor" water quality. The performance of the simplified NSF-WQI water quality index developed using artificial neural networks was evaluated using statistical criteria such as correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), indicating that the model produced good results. Keywords: Ordu Province, Water Quality, Hydrogeochemistry, Statistics, Water Quality Index, NSF-WQI, WAWQI, Artificial Neural Networks.