000 | 03458nam a22005295i 4500 | ||
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001 | 978-3-540-75384-1 | ||
003 | DE-He213 | ||
005 | 20170628034802.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2008 gw | s |||| 0|eng d | ||
020 |
_a9783540753841 _9978-3-540-75384-1 |
||
024 | 7 |
_a10.1007/978-3-540-75384-1 _2doi |
|
050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
072 | 7 |
_aTBJ _2bicssc |
|
072 | 7 |
_aMAT003000 _2bisacsh |
|
082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aCai, Xing. _eeditor. |
|
245 | 1 | 0 |
_aQuantitative Information Fusion for Hydrological Sciences _h[electronic resource] / _cedited by Xing Cai, T. -C. Jim Yeh. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008. |
|
300 |
_aIX, 218 p. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v79 |
|
505 | 0 | _aData 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. | |
520 | _aIn 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. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aHydraulic engineering. | |
650 | 0 | _aEngineering geology. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aEngineering mathematics. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aHydrogeology. |
650 | 2 | 4 | _aGeotechnical Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
700 | 1 |
_aYeh, T. -C. Jim. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540753834 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v79 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-75384-1 |
912 | _aZDB-2-ENG | ||
999 |
_c21511 _d21511 |