As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important pos...As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.展开更多
An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) mod...An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.展开更多
Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up base...Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.展开更多
The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow a...The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow and improve the water environment carrying capacity of Haishu Plain, the river network hydrodynamic model is used in this paper to simulate the water intake location, reasonable water quantity and influence range of water transfer in Haishu Plain. The simulation results have high accuracy, which can provide a scientific basis for the scale, water transfer mechanism and project layout of water transfer construction in Haishu Plain and show a strong reference value for the study of water diversion and distribution scheme of coastal plain river network.展开更多
Catchments health assessment is fundamental to effective catchments management. Generally, an as-sessment method should be selected to reflect both the purpose of assessment and local characteristics. A trial in Shang...Catchments health assessment is fundamental to effective catchments management. Generally, an as-sessment method should be selected to reflect both the purpose of assessment and local characteristics. A trial in Shanghai was conducted to test the method for catchments health assessment in urbanized river network area. Seven indicators that described four dimensions of river, river network, land use and function, and local feature were used to assess catchments values; while possible change rate of urbanization and industrialization in the next 3 years were chosen for catchments pressure assessment in the value-pressure model. Factors related to catchments classi-fication, indicators measurement and protection priority have been considered in the development strategies for catchments health management. The results showed that value-pressure assessment was applicable in urbanized catchments health management, particularly when both human and catchments had multiple demands. As a result of over 30-year rapid urbanization, more than 70% of Shanghai river network area was still in a healthy condition with high catchments values, among them, 39.3% was under high pressure. Poor water quality, simplified river system and weakened local feature of river pattern had largely affected catchments health in Shanghai. Lack of long-term monitoring data would seriously restrict the development and validity of catchments health assessment.展开更多
Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies diffic...Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies difficult. Generally, conceptual rain-flow models are designed to bring an appropriate answer with the correction of gaps and prediction of the flows. Historical hydro-climatic data of the Ity station, located on Cavally River, contain gaps which must be bridged. This study aims to establish a rainfall-runoff model through artificial neural networks for filling the gaps into the flow data series of the hydrometric station of Ity on the watershed of Cavally River. A multi-layer perceptron of feed forwards with two entries (monthly average rain and evapotranspiration) and an exit (flows) was established with flow evapotranspiration data. Comparison of the criteria of performance of the various architectures of the neural network model showed that architecture 2-3-1 gives best results. This architecture provides Nash coefficients of 75.79% and correlation linear coefficient of 95.64% for the calibration and Nash coefficients of 73.32% and correlation linear coefficient of 98.33% for the validation. The correlations between simulated flows and observed flows are strong. The correlation coefficients are 83.89% and 83.08% respectively for the calibration and validation.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
In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The c...In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.展开更多
In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method a...In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.展开更多
Recently, literature on urban network research from the perspective of ?rm networks has been increasing. This research mainly used data from the headquarters and branches of all 2581 listed manufacturing companies in ...Recently, literature on urban network research from the perspective of ?rm networks has been increasing. This research mainly used data from the headquarters and branches of all 2581 listed manufacturing companies in the Yangtze River Delta from 1990 to 2017, and studied the urban network through an interlocking network model that quantifies the links between enterprises. The results showed that the spatial distribution of listed manufacturing industries in the Yangtze River Delta was relatively concentrated, and cities such as Shanghai, Nanjing, and Hangzhou were hot spots for the spatial distribution of listed manufacturing industries. However, Fuyang, Suqian, Chizhou, Lishui and other network edge cities were less distributed in manufacturing. The urban network of the Yangtze River Delta has significant hierarchical characteristics. The urban network of the Yangtze River Delta presents a multi-center network development mode with Shanghai as the center and Nanjing, Hangzhou, and Hefei as the sub-centers. Moreover, we found that the development of inter-city connections in the Yangtze River Delta was driven by network mechanisms of priority attachment and path dependence. The radiating capacity and agglomeration capacity of cities in the Yangtze River Delta have a strong polarization characteristic. The core cities such as Shanghai, Nanjing, Hangzhou, and Hefei have much higher network radiation capabilities than network aggregation capabilities. However, other non-core cities and network edge cities have weak network radiation capabilities, and mainly accept network radiation from core cities. It enriches the research of urban networks based on real inter-?rm connections, and provides ideas for the wider regional study and the combination of econometric techniques and social network analysis.展开更多
Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature ...Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.展开更多
Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall co...Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.展开更多
基金National Natural Science Foundation of China(41901297,41806209)Science and Technology Key Project of Henan Province(202102310017)+1 种基金Key Research Projects for the Universities of Henan Province(20A170013)China Postdoctoral Science Foundation(2021M693201)。
文摘As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.
基金Funded by the Natural Science Foundation of China (No. 59778021)
文摘An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.
基金supported by the Major Program of the National Natural Science Foundation of China(Grant No.51190091)the National Natural Science Foundation of China(Grant No.51009045)the Open Research Fund Program of the State Key Laboratory of Water Resources and Hydropower Engineering Science of Wuhan University(Grant No.2012B094)
文摘Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.
文摘The water distribution network is an important part of the plain water environment improvement system. To make efficient use of the regional water diversion source, scientifically distribute the water diversion flow and improve the water environment carrying capacity of Haishu Plain, the river network hydrodynamic model is used in this paper to simulate the water intake location, reasonable water quantity and influence range of water transfer in Haishu Plain. The simulation results have high accuracy, which can provide a scientific basis for the scale, water transfer mechanism and project layout of water transfer construction in Haishu Plain and show a strong reference value for the study of water diversion and distribution scheme of coastal plain river network.
基金Under the auspices of Shanghai Natural Science Foundation (No. 09ZR1409100)National Natural Science Foundation of China (No. 40871016)Key Program of National Natural Science Foundation of China (No. 40730526)
文摘Catchments health assessment is fundamental to effective catchments management. Generally, an as-sessment method should be selected to reflect both the purpose of assessment and local characteristics. A trial in Shanghai was conducted to test the method for catchments health assessment in urbanized river network area. Seven indicators that described four dimensions of river, river network, land use and function, and local feature were used to assess catchments values; while possible change rate of urbanization and industrialization in the next 3 years were chosen for catchments pressure assessment in the value-pressure model. Factors related to catchments classi-fication, indicators measurement and protection priority have been considered in the development strategies for catchments health management. The results showed that value-pressure assessment was applicable in urbanized catchments health management, particularly when both human and catchments had multiple demands. As a result of over 30-year rapid urbanization, more than 70% of Shanghai river network area was still in a healthy condition with high catchments values, among them, 39.3% was under high pressure. Poor water quality, simplified river system and weakened local feature of river pattern had largely affected catchments health in Shanghai. Lack of long-term monitoring data would seriously restrict the development and validity of catchments health assessment.
文摘Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies difficult. Generally, conceptual rain-flow models are designed to bring an appropriate answer with the correction of gaps and prediction of the flows. Historical hydro-climatic data of the Ity station, located on Cavally River, contain gaps which must be bridged. This study aims to establish a rainfall-runoff model through artificial neural networks for filling the gaps into the flow data series of the hydrometric station of Ity on the watershed of Cavally River. A multi-layer perceptron of feed forwards with two entries (monthly average rain and evapotranspiration) and an exit (flows) was established with flow evapotranspiration data. Comparison of the criteria of performance of the various architectures of the neural network model showed that architecture 2-3-1 gives best results. This architecture provides Nash coefficients of 75.79% and correlation linear coefficient of 95.64% for the calibration and Nash coefficients of 73.32% and correlation linear coefficient of 98.33% for the validation. The correlations between simulated flows and observed flows are strong. The correlation coefficients are 83.89% and 83.08% respectively for the calibration and validation.
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.
文摘In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.
基金financially supporrted by the National Key Research and Development Program of China(Grant No.2017YFC1404200)the National Natural Science Foundation of China(Grant Nos.51779150 and 51979040)
文摘In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.
文摘Recently, literature on urban network research from the perspective of ?rm networks has been increasing. This research mainly used data from the headquarters and branches of all 2581 listed manufacturing companies in the Yangtze River Delta from 1990 to 2017, and studied the urban network through an interlocking network model that quantifies the links between enterprises. The results showed that the spatial distribution of listed manufacturing industries in the Yangtze River Delta was relatively concentrated, and cities such as Shanghai, Nanjing, and Hangzhou were hot spots for the spatial distribution of listed manufacturing industries. However, Fuyang, Suqian, Chizhou, Lishui and other network edge cities were less distributed in manufacturing. The urban network of the Yangtze River Delta has significant hierarchical characteristics. The urban network of the Yangtze River Delta presents a multi-center network development mode with Shanghai as the center and Nanjing, Hangzhou, and Hefei as the sub-centers. Moreover, we found that the development of inter-city connections in the Yangtze River Delta was driven by network mechanisms of priority attachment and path dependence. The radiating capacity and agglomeration capacity of cities in the Yangtze River Delta have a strong polarization characteristic. The core cities such as Shanghai, Nanjing, Hangzhou, and Hefei have much higher network radiation capabilities than network aggregation capabilities. However, other non-core cities and network edge cities have weak network radiation capabilities, and mainly accept network radiation from core cities. It enriches the research of urban networks based on real inter-?rm connections, and provides ideas for the wider regional study and the combination of econometric techniques and social network analysis.
基金Under the auspices of National Natural Science Foundation of China (No. 50809004)
文摘Taking the nonlinear nature of runoff system into account,and combining auto-regression method and multi-regression method,a Nonlinear Mixed Regression Model (NMR) was established to analyze the impact of temperature and precipitation changes on annual river runoff process. The model was calibrated and verified by using BP neural network with observed meteorological and runoff data from Daiying Hydrological Station in the Chaohe River of Hebei Province in 1956–2000. Compared with auto-regression model,linear multi-regression model and linear mixed regression model,NMR can improve forecasting precision remarkably. Therefore,the simulation of climate change scenarios was carried out by NMR. The results show that the nonlinear mixed regression model can simulate annual river runoff well.
文摘Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.