Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-M...Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-Marquant algorithm. The neural network model was proposed to evaluate DO in water. The model contains two parts: firstly, the learning sample is unified; secondly, the neural network is used to train the unified samples to ensure the best node number of hidden layer. The proposed model is applied to assessing the DO concentration of the Yellow River in Lanzhou city. The evaluation result is compared with that by the neural network method and the reported result in Lanzhou city. The comparison result indicates that the performance of the neural network model is practically feasible in the assessment of DO. At the same time, the linear interpolation method can add the number of network's learning sample to improve the prediction precision of the network.展开更多
Inorganic carbon, the great part of the riverine carbon exported to the ocean, plays an important role in the global carbon cycle and ultimately impacts the coupled carbon-climate system. An overview was made on both ...Inorganic carbon, the great part of the riverine carbon exported to the ocean, plays an important role in the global carbon cycle and ultimately impacts the coupled carbon-climate system. An overview was made on both methods and results of the riverine inorganic carbon researches. In addition to routine in situ survey, measurement and calculation, the direct precipitation method and the gas evolution technique were commonly used to analyze dissolved inorganic carbon in natural water samples. Soil CO2, carbonate minerals and atmospheric CO2 incorporated into riverine inorganic car- bon pool via different means, with bicarbonate ion being the dominant component. The concentration of inorganic carbon, the composition of carbon isotopes （8^13C and △^14C）, and their temporal or spatial variations in the streams were controlled by carbon input, output and changes of carbon biogeochemistry within the riverine system. More accurate flux es- timation, better understanding of different influential processes, and quantitative determination of various inputs or outputs need to be well researched in future.展开更多
文摘Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-Marquant algorithm. The neural network model was proposed to evaluate DO in water. The model contains two parts: firstly, the learning sample is unified; secondly, the neural network is used to train the unified samples to ensure the best node number of hidden layer. The proposed model is applied to assessing the DO concentration of the Yellow River in Lanzhou city. The evaluation result is compared with that by the neural network method and the reported result in Lanzhou city. The comparison result indicates that the performance of the neural network model is practically feasible in the assessment of DO. At the same time, the linear interpolation method can add the number of network's learning sample to improve the prediction precision of the network.
基金Under the auspices of the National Natural Science Foundation of China (No. 40471120), the Specialized ResearchFund for the Doctoral Program of Higher Education (No. 20040558025), the '985 Project' of GIS and Remote Sensing for Geo-sciences from the Ministry of Education of China (No. 105203200400006), and the Guangdong Provincial Natural Science Founda-tion (No. 031548)
文摘Inorganic carbon, the great part of the riverine carbon exported to the ocean, plays an important role in the global carbon cycle and ultimately impacts the coupled carbon-climate system. An overview was made on both methods and results of the riverine inorganic carbon researches. In addition to routine in situ survey, measurement and calculation, the direct precipitation method and the gas evolution technique were commonly used to analyze dissolved inorganic carbon in natural water samples. Soil CO2, carbonate minerals and atmospheric CO2 incorporated into riverine inorganic car- bon pool via different means, with bicarbonate ion being the dominant component. The concentration of inorganic carbon, the composition of carbon isotopes （8^13C and △^14C）, and their temporal or spatial variations in the streams were controlled by carbon input, output and changes of carbon biogeochemistry within the riverine system. More accurate flux es- timation, better understanding of different influential processes, and quantitative determination of various inputs or outputs need to be well researched in future.
