Quantitative Information Fusion for Hydrological Sciences
| Corporate Author: | |
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edition: | 1st ed. 2008. |
| Series: | Studies in Computational Intelligence,
79 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-540-75384-1 |
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-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.