Quantitative Information Fusion for Hydrological Sciences
Author(s)
Bibliographic Information
Quantitative Information Fusion for Hydrological Sciences
（Studies in computational intelligence, v79）
Springer, c2008
Note
with 81 Figures and 7 Tables
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"