The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression ...The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression model respectively for predicting the fresh water algae bloom. The different predicted abilities of the two methods in Liuhai lakes were compared. A principle analysis method was first used to select the input variables of the models to avoid the phenomenon of collinearity in the data. The results showed that the input variables for the artificial neural networks were T, TP, transparency（SD）, DO, chlorophyll-a （Chl-a）,pH and the output variable was Chl-a. A three layer Levenberg-Marguardt feed forward leaming algorithm in ANN was used to model the eutrophication process of Liuhai lakes. 20 nodes in hidden layer and 1 node of output for the ANN model had been optimized by trial and error method. A sensitivity analysis of the input variables was performed to evaluate their relative significance in determining the predicted values. The correlation coefficient between predicted value and observed value in all data and in test data were 0.717 and 0.816 respectively in the artificial neural networks. The stepwise regression method was used to simulate the linear relation between Chl-a and temperature, of which the correlation coefficient was 0.213. By comparing the results of the two models, it was found that neural network models were able to simulate non-linear behavior in the water eutrophication process of Liuhai lakes reasonably and could successfully estimate some extreme values from calibration and test data sets.展开更多
In order to simulate the characteristics of hydrodynamic field and mass transport processes in the Yuqiao Reservoir （YQR）, a 2-D coupled model of hydrodynamics and water quality was developed, and the water-quality ...In order to simulate the characteristics of hydrodynamic field and mass transport processes in the Yuqiao Reservoir （YQR）, a 2-D coupled model of hydrodynamics and water quality was developed, and the water-quality related state variables in this model included CODMn, TN and TP. The hydrodynamic model was driven by employing observed winds and daily measured flow data to simulate the seasonal water cycle of the reservoir. The simulation of the mass transport and transformation processes of CODMn, TN and TP was based on the unsteady diffusion equations, driven by observed meteorological forcing and external loadings, with the fluxes form the bottom of reservoir and the plant photosynthesis and respiration as internal sources and sinks. A finite volume method and Alternating Direction Implicit （ADI） scheme were used to solve these equations. The model was calibrated and verified by using the data observed from YQR in two different years. The results showed that in YQR, the wind-driven current was an important style of lake current, while the concentration of water quality item was decreasing from east to west because of the external pollutant loadings. There was a good agreement between the simulated and measured values, with the minimal calculation error percent of 0.1% and 2.6% and the mean error percent of 44.0% and 51.2% for TN and TP separately. The simulation also showed that, in YQR, the convection was the main process in estuaries of inflow river, and diffusion and biochemical processes dominate in center of reservoir. So it was necessary to build a pre-pond to reduce the external loadings into the reservoir.展开更多
文摘The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression model respectively for predicting the fresh water algae bloom. The different predicted abilities of the two methods in Liuhai lakes were compared. A principle analysis method was first used to select the input variables of the models to avoid the phenomenon of collinearity in the data. The results showed that the input variables for the artificial neural networks were T, TP, transparency（SD）, DO, chlorophyll-a （Chl-a）,pH and the output variable was Chl-a. A three layer Levenberg-Marguardt feed forward leaming algorithm in ANN was used to model the eutrophication process of Liuhai lakes. 20 nodes in hidden layer and 1 node of output for the ANN model had been optimized by trial and error method. A sensitivity analysis of the input variables was performed to evaluate their relative significance in determining the predicted values. The correlation coefficient between predicted value and observed value in all data and in test data were 0.717 and 0.816 respectively in the artificial neural networks. The stepwise regression method was used to simulate the linear relation between Chl-a and temperature, of which the correlation coefficient was 0.213. By comparing the results of the two models, it was found that neural network models were able to simulate non-linear behavior in the water eutrophication process of Liuhai lakes reasonably and could successfully estimate some extreme values from calibration and test data sets.
基金the National Basic Research Program of China (973 Program, Grant No. 2006CB403400)the National Natural Science Foundation of China (Grant Nos. 50679088, 90610037).
文摘In order to simulate the characteristics of hydrodynamic field and mass transport processes in the Yuqiao Reservoir （YQR）, a 2-D coupled model of hydrodynamics and water quality was developed, and the water-quality related state variables in this model included CODMn, TN and TP. The hydrodynamic model was driven by employing observed winds and daily measured flow data to simulate the seasonal water cycle of the reservoir. The simulation of the mass transport and transformation processes of CODMn, TN and TP was based on the unsteady diffusion equations, driven by observed meteorological forcing and external loadings, with the fluxes form the bottom of reservoir and the plant photosynthesis and respiration as internal sources and sinks. A finite volume method and Alternating Direction Implicit （ADI） scheme were used to solve these equations. The model was calibrated and verified by using the data observed from YQR in two different years. The results showed that in YQR, the wind-driven current was an important style of lake current, while the concentration of water quality item was decreasing from east to west because of the external pollutant loadings. There was a good agreement between the simulated and measured values, with the minimal calculation error percent of 0.1% and 2.6% and the mean error percent of 44.0% and 51.2% for TN and TP separately. The simulation also showed that, in YQR, the convection was the main process in estuaries of inflow river, and diffusion and biochemical processes dominate in center of reservoir. So it was necessary to build a pre-pond to reduce the external loadings into the reservoir.
