Quantitative Information Fusion for Hydrological Sciences

Quantitative Information Fusion for Hydrological Sciences

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Cai, Xing. (Editor ), Jim Yeh, T.-C. (Editor )
Formato: eBook
Lenguaje:English
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Studies in Computational Intelligence, 79
Materias:
Hydrogeology.
Applied mathematics.
Engineering mathematics.
Hydrology.
Geotechnical engineering.
Artificial intelligence.
Mathematical and Computational Engineering.
Hydrology/Water Resources.
Geotechnical Engineering & Applied Earth Sciences.
Artificial Intelligence.
Acceso en línea:https://doi.org/10.1007/978-3-540-75384-1
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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