Flow resistance in open channels with dune bedform is a substantial issue due to the influence of dunes on the hydraulic roughness, which can affect the performance of hydraulic constructions.There are a number of nonlinear approaches that have been developed to predict the roughness coefficient in alluvial channels, such as developed equations based on the mean velocity or shear stresses. However, due to the multitude of factors influencing roughness, establishing an accurate determination of the roughness coefficient is difficult. This study applies gene expression programing (GEP) and nonlinear approaches to predict the Manning's coefficient in dune bedform channels. Four different experimental data series were used for modeling. In order to develop the models, three scenarios with different input combinations were considered: scenario 1 considers only flow characteristics, scenario 2 considers flow and bedform characteristics, and scenario 3 considers flow and sediment characteristics. The results proved that GEP is capable of predicting the Manning's coefficient. It was found that for estimation of the roughness coefficient in dune bedform channels, scenario 3 performed more successfully than others. Sensitivity analysis showed that the Reynolds number plays a key role in the modeling process. Comparisons between GEP models and existing equations indicated that GEP models yield better results. Figure 7 | Scatter plot of predicted Manning's coefficient vs. observed values; flow and bedform characteristics.367 K. Roushangar et al. | Prediction of flow resistance in open channel with dune bedform
An accurate prediction of roughness coefficient in alluvial channels is of substantial importance for river management. In this study, the total and form resistance in alluvial channels with dune bedform were assessed using experimental data. First, the data of experiments carried out at the Hydraulic Laboratory of University of Tabriz was used to investigate the impact of hydraulic and sediment parameters on roughness coefficient. Then, these data were combined with other laboratory data, and the total and bedform resistance were modeled via a Gaussian Process Regression (GPR) approach. For models, developing different input combinations were considered based on flow and sediment characteristics. The obtained results from the experiments showed that the Reynolds number has a better correlation with flow resistance in comparison with other hydraulic parameters. It was found that the roughness variations due to bedform are almost between 40 and 80% of the total roughness coefficient. Also, the obtained results proved the capability of the GPR method in the modeling process. It was found that the model which took the advantages of both flow and sediment characteristics performed better compared to the other models. The sensitivity analysis results showed that the Reynolds number has the most significant impact in the prediction process.
Wastewater Treatment Plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. In recent decades, Artificial Intelligence (AI) approaches have been used as effective tools in order to investigate environmental engineering issues. In this study, the effluent quality of Tabriz Wastewater Treatment Plant (Tabriz WWTP) was assessed using two intelligence models namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. Three daily, weekly, and monthly time scales were investigated in the modeling process. On the other hand, since applied methods were sensitive to input variables, therefore, the Monte-Carlo uncertainty analysis method was used to investigate the best-applied model dependability. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP.
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