Quantitative Information Fusion for Hydrological Sciences
著者
書誌事項
Quantitative Information Fusion for Hydrological Sciences
（Studies in computational intelligence, v79）
Springer, c2008
注記
with 81 Figures and 7 Tables
内容説明・目次
内容説明
In this rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? Here, two highly qualified scientists edit a volume that takes the angle of computational hydrology and envision one of the science's future directions - namely, the 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.
目次
Data Fusion Methods for Integrating Data-driven Hydrological Models.- A New Paradigm for Groundwater Modeling.- Information Fusion using the Kalman Filter based on Karhunen-Loeve Decomposition.- Trajectory-Based Methods for Modeling and Characterization.- The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology.- Information Fusion in Regularized Inversion of Tomographic Pumping Tests.- Advancing the Use of Satellite Rainfall Datasets for Flood Prediction in Ungauged Basins: The Role of Scale, Hydrologic Process Controls and the Global Precipitation Measurement Mission.- Integrated Methods for Urban Groundwater Management Considering Subsurface Heterogeneity.
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