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Quantitative Information Fusion for Hydrological Sciences

  • Book
  • © 2008

Overview

Editors:
  1. Xing Cai

    1. Simula Research Laboratory, Lysaker, Norway
      Department of Informatics, University of Oslo, Oslo, Norway

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  2. T. -C. Jim Yeh

    1. Department of Hydrology and Water Resources, The University of Arizona, Tucson, USA

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  • edited overview about quantitative information fusion in hydrology
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 79)

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Table of contents (8 chapters)

  1. Front Matter

    Pages I-IX
    Download chapter PDF

  2. Data Fusion Methods for Integrating Data-driven Hydrological Models

    • Linda See
    Pages 1-18

  3. A New Paradigm for Groundwater Modeling

    • Shu-Guang Li, Qun Liu
    Pages 19-41

  4. Information Fusion using the Kalman Filter based on Karhunen-Loève Decomposition

    • Zhiming Lu, Dongxiao Zhang, Yan Chen
    Pages 43-68

  5. Trajectory-Based Methods for Modeling and Characterization

    • D. W. Vasco
    Pages 69-103

  6. The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology

    • Akhil Datta-Gupta, Deepak Devegowda, Dayo Oyerinde, Hao Cheng
    Pages 105-136

  7. Information Fusion in Regularized Inversion of Tomographic Pumping Tests

    • Geoffrey C. Bohling
    Pages 137-162

  8. 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

    • Faisal Hossain, Nitin Katiyar
    Pages 163-181

  9. Integrated Methods for Urban Groundwater Management Considering Subsurface Heterogeneity

    • Jannis Epting, Peter Huggenberger, Christian Regli, Natalie Spoljaric, Ralph Kirchhofer
    Pages 183-218
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Keywords

About this book

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, chemical, atmospheric, and biological information to characterize and predict natural systems in hydrological sciences.

Intelligent computation and information fusion are the key words. The aim is to provide both established scientists and graduate students with a summary of recent developments in this topic. The chapters of this edited volume cover some of the most important ingredients for quantitative hydrological information fusion, including data fusion techniques, interactive computational environments, and supporting mathematical and numerical methods. Real-life applications of hydrological information fusion are also addressed.

Editors and Affiliations

  • Simula Research Laboratory, Lysaker, Norway

    Xing Cai

  • Department of Informatics, University of Oslo, Oslo, Norway

    Xing Cai

  • Department of Hydrology and Water Resources, The University of Arizona, Tucson, USA

    T. -C. Jim Yeh

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