000 | 03634nam a2200493 i 4500 | ||
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001 | 6308075 | ||
003 | IEEE | ||
005 | 20190220121650.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 151223s2003 mauab ob 001 eng d | ||
020 |
_z0262271915 _qelectronic |
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020 |
_z9780585341019 _qelectronic |
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020 |
_z058534101X _qelectronic |
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020 |
_a9780262271912 _qelectronic |
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020 |
_z9780262515726 _qprint |
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035 | _a(CaBNVSL)mat06308075 | ||
035 | _a(IDAMS)0b0000648190889b | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aQE48.8 _b.D68 1995eb |
|
100 | 1 |
_aDowla, Farid U., _eauthor. |
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245 | 1 | 0 |
_aSolving problems in environmental engineering and geosciences with artificial neural networks / _cFarid U. Dowla and Leah L. Rogers. |
264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2003] |
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264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc1995. |
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300 |
_a1 PDF (x, 239 pages) : _billustrations, maps. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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504 | _aIncludes bibliographical references and index. | ||
506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
520 | _aArtificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions.Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented. | ||
530 | _aAlso available in print. | ||
538 | _aMode of access: World Wide Web | ||
588 | _aDescription based on PDF viewed 12/23/2015. | ||
650 | 0 | _aNeural networks (Computer science) | |
650 | 0 |
_aEnvironmental engineering _xData processing. |
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650 | 0 |
_aEarth sciences _xData processing. |
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655 | 0 | _aElectronic books. | |
700 | 1 | _aRogers, Leah L. | |
710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. |
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710 | 2 |
_aMIT Press, _epublisher. |
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776 | 0 | 8 |
_iPrint version _z9780262515726 |
856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6308075 |
999 |
_c39550 _d39550 |