Titlebook: Quantitative Information Fusion for Hydrological Sciences; Xing Cai,T. -C. Jim Yeh Book 2008 Springer-Verlag Berlin Heidelberg 2008 Ground

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书目名称Quantitative Information Fusion for Hydrological Sciences影响因子(影响力)




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书目名称Quantitative Information Fusion for Hydrological Sciences被引频次




书目名称Quantitative Information Fusion for Hydrological Sciences被引频次学科排名




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书目名称Quantitative Information Fusion for Hydrological Sciences年度引用学科排名




书目名称Quantitative Information Fusion for Hydrological Sciences读者反馈




书目名称Quantitative Information Fusion for Hydrological Sciences读者反馈学科排名

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Book 2008hydrology and envisions one of the future directions, namely, quantitative integration of high-quality hydrologic field data with geologic, hydrologic, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences...Intelligent computation and

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Integrated Methods for Urban Groundwater Management Considering Subsurface Heterogeneity,owledge of groundwater flow regimes could lead to reducing and minimizing, as far as possible, the negative impacts throughout the construction phases, and to developing sustainable groundwater use and management tools.

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Data Fusion Methods for Integrating Data-driven Hydrological Models,g, neural networks, fuzzy logic, M5 model trees and instance-based learning. The results show that the data fusion approaches produce better performing models compared to the individual models on their own. The potential of this approach is demonstrated yet remains largely unexplored in real-time hydrological forecasting.

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Data Fusion Methods for Integrating Data-driven Hydrological Models,emonstrated using flow forecasting models from the River Ouse catchment in the UK for a lead time of 6 hours. These approaches include simple averaging, neural networks, fuzzy logic, M5 model trees and instance-based learning. The results show that the data fusion approaches produce better performin

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A New Paradigm for Groundwater Modeling,fic computing to be real-time interactive with the modelers being dynamically-engaged and in full control throughout the computational process. The report stressed: scientists not only want to solve equations or analyze data that results from computing, they also want to interpret what is happening

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