Archives for Technical Sciences | 2026
Authors: Shakir A.N.; Mirdan O.F.; Omar A.S.
DOI: 10.70102/afts.2026.1835.405
Journal: Archives for Technical Sciences
Year: 2026
Publisher: Technical institute of Bijeljina
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
In Kirkuk City, Iraq, groundwater pollution is increasingly a problem due to intense urbanization, population growth, and unregulated water well drilling. Although the systematic assessment of groundwater pollution is essential to domestic and agricultural applications, the systematic analysis of its contamination has not been conducted, and the currently available approaches are not able to determine the high-risk zones. This paper involves Geographic Information Systems (GIS) and Multi-Objective Particle Swarm Optimization (MOPSO) to develop optimization risk maps of groundwater contamination. Data on key water quality parameters pH (7.10–8.30), turbidity (5.30–190.50 NTU), electrical conductivity (EC) (681.40–4245.87 µS/cm), total dissolved solids (TDS) (476.78–2563.5 mg/l), calcium (Ca) (31.33–256.33 mg/l), and chloride (Cl) (10.3–192.4 mg/l) were collected from 43 wells in the city. Kriging and Inverse Distance Weighting (IDW) were used as spatial interpolation methods, and then MOPSO was used to optimize the weights of the parameters to produce a more accurate risk mapping. The findings revealed that the central and southern areas were more contaminated, with TDS reaching up to 1477 mg/L and chloride up to 192.4 mg/L. On the other hand, contamination was lower in the northern and eastern parts, with TDS values of approximately 264 mg/l. The incorporation of MOPSO improved the reliability of groundwater risk predictions, providing a superior decision-making aid. This paper outlines how MOPSO and GIS can be used to manage groundwater effectively, which shows more precise risk evaluation. To improve predictions of long-term groundwater contamination, future studies should incorporate real-time monitoring and account for seasonal changes. © 2026, Technical institute of Bijeljina. All rights reserved.
contamination prediction; GIS (geographic information systems); groundwater contamination; MOPSO (multi-objective particle swarm optimization); risk mapping; spatial interpolation; TDS (total dissolved solids); water quality parameters