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چکیده
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The Alavian Dam is a critical water infrastructure in East Azarbaijan Province of Iran, providing essential water resources in Lake Urmia basin. In the present research, the detailed flow and sediment data available from 1990 to 2018 have been analyzed for sediment transport using different models ranging from traditional techniques to modern soft computing approaches. The best overall performance was obtained with the Modified Einstein Procedure, with R2 = 0.89 and NSE = 0.83 for the sediment load estimates. A power-law function results with an exponent of 1.24 between the discharge and suspendedsediment concentration, indicating a highly nonlinear response. Seasonal analysis shows that 68% of the annual sediment load is transported during spring months. Extreme event analysis using a log-Pearson Type III distribution indicated that high-magnitude, low-frequency events are very important to long-term sediment budgets; the 100-year sediment load was estimated to be 65,000 tons/day. Finally, applying the best sediment transport model, this study projected that the Alavian Dam reservoir would lose 50% of its useful storage capacity in about 235 years assuming no intervention. However, when performing sensitivity analysis with regard to possible effects of climate change and variable trap efficiencies, the findings go up to the range of 182–284 years. In order to enhance the predictive power of these findings for transferability of output, incorporation of soft computing methods has been done using Gene Expression Programming. The results indicated that the GEP model could achieve an R2 of 0.91 and an NSE of 0.85 for sediment load estimation, which is rather promising for application on problems related to reservoir management. The study's comprehensive sediment transport analysis and predictive modeling provide essential tools for the dam operators to implement targeted seasonal sediment management strategies, potentially extending the dam's effective lifespan beyond current projections. Furthermore, the findings enable evidence-based decision-making for future infrastructure investments and help optimize the timing of sediment management interventions, potentially saving millions in maintenance costs over the dam's lifetime.
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