Abstract
This study presents an advanced optimization model to assess the impacts of climate change on water quality in the Al-Hilla River. To reduce uncertainties associated with climate change projections and quantify the river's water quality response, a novel model of the river system is developed, with the objective function integrated into optimization theory. Water quality simulations for different regions of the river system are performed using the QUAL2K model, while the Ant Colony Optimization (ACO) method is applied to optimize the model. Additionally, the study investigates the effects of temperature and DO variations on microbial populations and the self-purification capacity of the water body. The results indicate that all climate change scenarios lead to a decline in water quality, with significant reductions in dissolved oxygen (DO) levels, even under safe discharge conditions. The study demonstrates that the proposed technique can identify optimal solutions more efficiently, contributing to faster and more reliable decision-making in water quality management. Also, the findings reveal that both temperature and DO significantly influence microbial composition and self-purification processes, with higher temperatures and DO levels improving self-purification efficiency. These insights enhance our understanding of the complex interactions between environmental factors and water quality, offering a valuable foundation for future water management strategies aimed at mitigating the effects of climate change.
Published Version
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