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 (R2), 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.
