A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time s...A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analyzed by the WNN model. The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables. The hybrid model (WNN) was compared with the standard ANN and AR models. The WNN model was able to provide a good fit with the observed data, especially the peak values during the testing period. The benchmark results from WNN model applications showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models (ANN and AR).展开更多
In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi...In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.展开更多
The Jialu River in China has been seriously polluted by the direct discharge of industrial and domestic wastewater. The predominant contaminants of the Jialu River and its adjacent groundwater were recently investigat...The Jialu River in China has been seriously polluted by the direct discharge of industrial and domestic wastewater. The predominant contaminants of the Jialu River and its adjacent groundwater were recently investigated. However, the potential genotoxic impact of polluted water on human health remains to be clarified. Here, we used human–hamster hybrid（AL） cells, which are sensitive for detecting environmental mutagens. We found that the cytotoxicity and mutagenicity of the groundwater in the Jialu River basin were influenced by the infiltration of the Jialu River. Hydrological periods significantly affected the cytotoxicity, but not the mutagenic potential, of surface and groundwater. Further, the mutagenic potential of groundwater samples located 〈 1 km from the Jialu River（S（M-2） water samples） was detected earlier than that of groundwater samples located approximately 20 km from the Jialu River（SN water samples）. Because of high cytotoxicity, the mutagenic potential of water samples from the Jialu River（S（M-1） water samples） was not significantly enhanced compared with that of untreated controls. To further assess the mutagenic dispersion potential, an artificial neural network model was adopted. The results showed that the highest mutagenic potential of groundwater was observed approximately 10 km from the Jialu River. Although further investigation of mutagenic spatial dispersion is required, our data are significant for advancing our understanding of the origin, dispersion,and biological effects of water samples from polluted areas.展开更多
On the basis of Digital Elevation Model （DEM） data, watershed delineation and spatial topological relationship were proposed by the Digital Elevation Drainage Network Model （DEDNM） for the area upstream of the Han...On the basis of Digital Elevation Model （DEM） data, watershed delineation and spatial topological relationship were proposed by the Digital Elevation Drainage Network Model （DEDNM） for the area upstream of the Hanzhong Hydrological Station in the Hanjiang River in China. Then, the Muskingum-Cunge method considering lateral flow into the river was applied to flood routing on the platform of digital basin derived from DEDNM. Because of considering lateral flow into the river, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. With a routing-after-superposition algorithm, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. As a result, the digital basin coupled with the Muskingum-Cunge method provides a better platform for water resources management and flood control.展开更多
文摘A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analyzed by the WNN model. The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables. The hybrid model (WNN) was compared with the standard ANN and AR models. The WNN model was able to provide a good fit with the observed data, especially the peak values during the testing period. The benchmark results from WNN model applications showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models (ANN and AR).
基金supported by the Water Conservancy Science and Technology Project of Jiangsu Province(Grant No.2012041)the Jiangsu Province Ordinary University Graduate Student Research Innovation Project(Grant No.CXZZ13_0256)
文摘In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.
基金supported by the CAS Strategic Priority Research Program(No.XDB14030502)the National Basic Research Program(973)of China(No.2014CB932002)+5 种基金the Natural Science Foundation of Anhui Province(No.1808085QB37)the Hi-Tech Research and Development Program(863)of China(No.2008AA062504)the National Natural Science Foundation of China(Nos.20977093,81273004,81301182,and 31470829)the Talent Research Foundation of Hefei University(No.16-17RC03)the Key Project of Natural Science Research of Anhui High Education Institutions(Nos.KJ2017A545,and KJ2017A546)the Project of Anhui Quality Engineering(No.2016msgzs060)
文摘The Jialu River in China has been seriously polluted by the direct discharge of industrial and domestic wastewater. The predominant contaminants of the Jialu River and its adjacent groundwater were recently investigated. However, the potential genotoxic impact of polluted water on human health remains to be clarified. Here, we used human–hamster hybrid（AL） cells, which are sensitive for detecting environmental mutagens. We found that the cytotoxicity and mutagenicity of the groundwater in the Jialu River basin were influenced by the infiltration of the Jialu River. Hydrological periods significantly affected the cytotoxicity, but not the mutagenic potential, of surface and groundwater. Further, the mutagenic potential of groundwater samples located 〈 1 km from the Jialu River（S（M-2） water samples） was detected earlier than that of groundwater samples located approximately 20 km from the Jialu River（SN water samples）. Because of high cytotoxicity, the mutagenic potential of water samples from the Jialu River（S（M-1） water samples） was not significantly enhanced compared with that of untreated controls. To further assess the mutagenic dispersion potential, an artificial neural network model was adopted. The results showed that the highest mutagenic potential of groundwater was observed approximately 10 km from the Jialu River. Although further investigation of mutagenic spatial dispersion is required, our data are significant for advancing our understanding of the origin, dispersion,and biological effects of water samples from polluted areas.
基金Project supported by the National Natural Science Foundation of China (Grant No :40171016) and the National KeyBasic Research Programof China (Grant No :2006CB400502) .
文摘On the basis of Digital Elevation Model （DEM） data, watershed delineation and spatial topological relationship were proposed by the Digital Elevation Drainage Network Model （DEDNM） for the area upstream of the Hanzhong Hydrological Station in the Hanjiang River in China. Then, the Muskingum-Cunge method considering lateral flow into the river was applied to flood routing on the platform of digital basin derived from DEDNM. Because of considering lateral flow into the river, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. With a routing-after-superposition algorithm, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. As a result, the digital basin coupled with the Muskingum-Cunge method provides a better platform for water resources management and flood control.
