Quantitative Information Fusion for Hydrological Sciences

In a rapidly evolving world of knowledge and technology, do you ever wonder how hydrology is catching up? This book takes the angle of computational hydrology and envisions one of the future directions, namely, quantitative integration of high-quality hydrologic field data with geologic, hydrologic,...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Cai, Xing (Editor ), Jim Yeh, T.-C (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Studies in Computational Intelligence, 79
Temas:
Geology.
Engineering mathematics.
Engineering-Data processing.
Water.
Hydrology.
Geotechnical engineering.
Artificial intelligence.
Mathematical and Computational Engineering Applications.
Geotechnical Engineering and Applied Earth Sciences.
Artificial Intelligence.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Data Fusion Methods for Integrating Data-driven Hydrological Models
  • A New Paradigm for Groundwater Modeling
  • Information Fusion using the Kalman Filter based on Karhunen-Loève 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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