The five main types of antisunward propagating energetic fluxes (particles and emission) may be thought of as well established to date, the effects of which lead to a particilar character of disturbance in the near-te...The five main types of antisunward propagating energetic fluxes (particles and emission) may be thought of as well established to date, the effects of which lead to a particilar character of disturbance in the near-terrestrial environment (the Earth's magnetosphere, ionosphere and atmosphere). The strongest global restructuring of the magnetosphere and ionosphere is caused by fluxes of relatively dense n of 1-70 cm-3 at the Earth's orbit) Solar Wind (SW) quasi-neutral, low-energy (E ＜ 10 keV) plasma which cause magnetospheric and ionospheric storms lasting 24 hours or longer. For that reason, main attention is given to their study at the initial stage of research. The physical essence of the method of predicting disturbances in the near-terrestrial space environment, the amplitude of which can be expressed in, for example, the Kp index units, involves:(1) identifying all the most geo-effective SW streams of type, (2) determing their sources on the solar disk,and (3) quantifying the correlations between the characteristics of their solar sources with a maximum value of the Kp-index that is caused by the concerned type of SW stream. Semi-phenomenological relations have been obtained, which relate parameters of type SW stream sources to characteristics of geomagnetic storms:storm commencement, the time at which the storm intensity reaches its maximum values, the storm duration,as well as to the storm amplitude expressed in terms of geomagnetic indeces.展开更多
Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to floodin...Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.展开更多
提出并行算法MSSF-VQ(Multiple Sequential Stream Forecast algorithm based on Vector Quantization),以解决多维序列流的未来趋势预测问题.算法利用矢量空间表示序列流的计算模型,并采用量子化技术离散处理连续序列流,然后提出了序...提出并行算法MSSF-VQ(Multiple Sequential Stream Forecast algorithm based on Vector Quantization),以解决多维序列流的未来趋势预测问题.算法利用矢量空间表示序列流的计算模型,并采用量子化技术离散处理连续序列流,然后提出了序列流矢量概率树的构造算法和搜索算法,最后阐述了算法步骤.真实流序列上的实验结果表明,MSSF-VQ算法预测的准确率高,速度快,在线处理占用的空间小,并有良好的扩展性.展开更多
基金Supported by the China-Russia Joint Research Center on Space Weather,Chinese Academy of Sciences
文摘The five main types of antisunward propagating energetic fluxes (particles and emission) may be thought of as well established to date, the effects of which lead to a particilar character of disturbance in the near-terrestrial environment (the Earth's magnetosphere, ionosphere and atmosphere). The strongest global restructuring of the magnetosphere and ionosphere is caused by fluxes of relatively dense n of 1-70 cm-3 at the Earth's orbit) Solar Wind (SW) quasi-neutral, low-energy (E ＜ 10 keV) plasma which cause magnetospheric and ionospheric storms lasting 24 hours or longer. For that reason, main attention is given to their study at the initial stage of research. The physical essence of the method of predicting disturbances in the near-terrestrial space environment, the amplitude of which can be expressed in, for example, the Kp index units, involves:(1) identifying all the most geo-effective SW streams of type, (2) determing their sources on the solar disk,and (3) quantifying the correlations between the characteristics of their solar sources with a maximum value of the Kp-index that is caused by the concerned type of SW stream. Semi-phenomenological relations have been obtained, which relate parameters of type SW stream sources to characteristics of geomagnetic storms:storm commencement, the time at which the storm intensity reaches its maximum values, the storm duration,as well as to the storm amplitude expressed in terms of geomagnetic indeces.
文摘Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.
文摘提出并行算法MSSF-VQ(Multiple Sequential Stream Forecast algorithm based on Vector Quantization),以解决多维序列流的未来趋势预测问题.算法利用矢量空间表示序列流的计算模型,并采用量子化技术离散处理连续序列流,然后提出了序列流矢量概率树的构造算法和搜索算法,最后阐述了算法步骤.真实流序列上的实验结果表明,MSSF-VQ算法预测的准确率高,速度快,在线处理占用的空间小,并有良好的扩展性.
