The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.
Bahmanshir estuary, which is connected to the Persian Gulf, is one of the most important water resources in region. In this study, saltwater intrusion due to possible sea level rise in the Bahmanshir estuary was investigated. A one-dimensional hydrodynamic and water quality model was used for the simulation of the salinity intrusion and associated water quality, with measured field data being used for model calibration and verification. The verified model was then used as a virtual laboratory to study the effects of different parameters on the salinity intrusion. A coupled gas-cycle/climate model was used to generate the climate change scenarios in the studied area that showed sea level rises varying from 30 to 90 cm for 2100. The models were then combined to assess the impact of future sea level rise on the salinity distribution in the Bahmanshir estuary. Using important dimensionless numbers, a dimensionally homogenous equation was subsequently developed for the prediction of the salinity intrusion length, showing that the salinity intrusion length is inversely correlated with the discharge and directly with the sea level rise. In addition, the magnitude and frequency of the salinity standard violations at the pump station were predicted for 2100, showing that the salinity violations under climate change effects can increase to 45 % of the times at this location. This reveals the importance of this type of approach for considering future infrastructure management.
ABSTRACT:Tidal excursion is an important parameter that indicates hydraulic and mixing characteristics of estuarine environments. Prediction of the tidal excursion length provides a proper tool for environmental management of estuaries. In this study, the governing equations of the salinity transport were scaled first to recognize the effective dimensionless parameters of tidal excursion length. Then, a laterally averaged two-dimensional numerical model called CE-QUAL-W2 was used as a virtual laboratory to simulate the salinity intrusion length. Existing field data of Limpopo estuary, as a case study, was used for calibration and verification of the model and reasonable agreement was observed between the model results and the field data. Finally, the verified model was used to assess the influences of the governing parameters. The results showed that simple power functions can be used to describe the effects of dimensionless parameters obtained by scaling of the governing equations. As a result, a new formula in form of a power function was derived to predict the tidal excursion length based on the geometric and hydrodynamic characteristics of alluvial estuaries. Comparison of the computed tidal excursion lengths using the derived formula with the observed measurements in several estuaries showed the robustness of the developed formula.
The main parameters that affect the salinity intrusion in estuaries are their geometric, hydrologic and hydrodynamic characteristics. The recognition of effective parameters and understanding their roles in the salinity intrusion are required for estuarine water management. In this study, the governing equations of the salinity intrusion processes were scaled to derive the effective dimensionless parameters. Then, a previously verified model, CE-QUAL-W2, was utilized as a virtual laboratory to investigate the effects of different governing parameters on the salinity intrusion. Analysis of the results showed that logarithmic functions can be used to describe the effect of dimensionless parameters obtained by scaling of governing equations. Finally, a formula was suggested to predict the salinity intrusion length based on geometrical and hydrodynamic characteristics of alluvial estuaries.
Reservoirs provide rural and municipal water supply for various purposes such as drinking water, irrigation, hydropower, industrial purposes and recreational activities. Supplying these demands depends strongly on the dam reservoir capacity. Hence, reservoir storage capacity prediction is a determining factor in water resources planning and management, drought risk management, flood risk assessment and management. In the present study, imperialist competitive algorithm as a relatively new socio-political-based global search technique introduced for solving different optimization problems employed to predict reservoir storage capacity of Shaharchay dam located in the Urmia lake basin in northwest of Iran. The high convergence rate of imperialist competitive algorithm along with its capability in finding global optimal is striking aspect of the algorithm. The results obtained from this algorithm were compared with those of Artificial Neural Network. The comparison of the results with the measured ones by means of error measures indicates the superiority of imperialist competitive algorithm over Artificial Neural Network.
2 3Salinity is an important parameter influencing the water quality of estuaries. Prediction of salt intrusion length in estuaries is a challenge for managers as well as scientists in this field. Several numerical and empirical models have been developed for the prediction of salt intrusion length in recent decades. The aim of this study is to evaluate the performance of empirical models in the Arvand river estuary in south-west Iran. A laterally averaged, twodimensional hydrodynamics-water quality model called CE-QUAL-W2 was used for numerical simulations. Salt intrusion lengths under different discharge and tidal conditions were first predicted by the validated numerical model, then the semi-empirical models were applied to the same hydrological/tidal conditions. From the comparison of the results, it was found that the model of Savenije, that properly considers the variation of bathymetry along the estuary, outperformed the others and could be used as a rapid assessment tool in the Arvand river estuary.
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