Modelling Euphrates river water quality index based on field measured data in Al-Diwaniyah City, Iraq.

Pollution monitoring in surface water using field observational procedure is a challenging matter as it is time consuming, and needs a lot of efforts. This study addresses the challenge of efficiently monitoring and predicting water pollution using a GIS-based artificial neural network (ANN) to detect heavy metal (HM) pollution in surface water and effect of wastewater required discharge on the Euphrates River in Al-Diwaniyah City, Iraq. The study established using 40 water sampling stations and incorporates Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES) to assess HM levels. An ANN model suggested to estimate Heavy Metal Pollution Index (HPI) considering physiological and chemical factors. It formulates six scenarios to enhance HPI prediction accuracy, utilizing ANN in MATLAB for modeling and GIS statistical tools with inverse distance weighted (IDW) methods for a comprehensive assessment. The developed approach predicted HP concentration in the Euphrates River basin in an actual case study. The validation of the predictive maps between the theoretical and practical part is performed by monitoring 16 stations and conducting laboratory analyses, resulting in acceptable coefficients of determination (R), observations standard deviation ratio (RSR), and Nash-Sutcliffe efficiency coefficients of 0.999, 1, and 0.99, respectively indicates that reliable forecast results closely match observed data from monitoring stations. The study identifies that nickel, iron, and cadmium concentrations exceeded Iraqi and World Health Organization (WHO) standards, leading to a heavy pollution index peak of 150.38 in the Euphrates River branch. In this study, the HPI is used to identify areas with high pollution levels, validate the accuracy of the ANN model for prediction, and generate a pollution map to visualize pollution levels.