Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with previous models were discussed in TMC estimation by using a wide range of hydraulic and geometrical data from field and laboratory experiments. According to statistical analysis, the performance of the mentioned models in both straight and meandering streams was more accurate than the regression-based models. Sensitivity analysis showed that the accuracy of the GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters. Eliminating the uncorrelated parameters and reducing the number of input parameters will reduce the complexity of the problem and improve the TMC estimation by GA-SVM.
In this study, the artificial neural network (ANN) method was applied to investigate the impacts of climate change on the water quantity and quality of the Qu'Appelle River in Saskatchewan, Canada. First, the second-generation Canadian earth system model (CanESM2) was adopted to predict future climate conditions. The Statistical DownScaling Model (SDSM) was then applied to downscale the generated data. To analyze the water quality of the river, concentrations of dissolved oxygen (DO) and total dissolved solids (TDSs) from the river were collected. Using the collected climate and hydrometric data, the ANNs were trained to simulate (i) the ratio of snowfall-to-total precipitation based on the temperature, (ii) the river flow rate based on the temperature and precipitation; and (iii) DO and TDS concentrations based on the river flow and temperature. Finally, the generated climate change data were used as inputs to the ANN model to investigate the climate change impacts on the river flow as well as DO and TDS concentrations within the selected region. Hydrologic alteration of the river was evaluated via the Range of Variability Approach (RVA) under historical and climate change scenarios. The results under climate change scenarios were compared with those under historical scenarios and indicated that climate change would lead to a heterogeneous change in precipitation and temperature patterns. These changes would have serious degrading impacts on the river discharge as well as DO and TDS concentration levels, causing deterioration in the sustainability of the river system and ecological health of the region.
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