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چکیده
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The sediment transport and the relation to water quality parameters and hydrological characteristics in the Sufi Chay River in Iran were investigated in this study using long-term monitoring data. Traditional statistical methods, dimensionless parameter analysis, and advanced soft computing techniques are combined within the scope of the presented comprehensive analysis. Total sediment load is the dependent variable, while the independent variables include flow rate, total dissolved solids (TDS), electrical conductivity (EC), pH, total anions, total cations, anion hardness, and cation hardness. Strong correlations were observed between total sediment load and flow rate (r = 0.82), total dissolved solids (r = 0.68), and electrical conductivity (r = 0.65). The dimensionless equation developed related sediment concentration to Reynolds number, Froude number, and normalized water quality parameters. The performance was quite good as revealed by the R2 value of 0.82. Comparison of performances using three soft computing methods, namely Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Support Vector Regression, are performed. The highest R2 value of 0.91 and RMSE of 53.2 tons/ day were obtained with ANFIS model. Sensitivity analyses indicated that flow rate and TDS were the most sensitive parameters to predict total sediment load. Generally, a seasonal variability in sediment transport, showing that the maximum discharges happened in the spring season with the mean of 187.3 tons/day, while the minimum discharges happened in the summer season with the mean of 42.8 tons/day. Besides, a nonlinear relationship between flow rate and both sediment concentration and discharge in this catchment reflects a complex erosion and transport process. The investigation also resulted in some important ion-parameter relationships, which are indicative of the geochemical factors operating on the water quality and sediment activity.
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