000			03709nam a2200505 i 4500
001			6308075
003			IEEE
005			20220712204800.0
006			m o d
007			cr |n|||||||||
008			151223s2003 mauab ob 001 eng d
020			_a9780262271912 _qelectronic
020			_z058534101X _qelectronic
020			_z9780585341019 _qelectronic
020			_z0262271915 _qelectronic
020			_z9780262515726 _qprint
035			_a(CaBNVSL)mat06308075
035			_a(IDAMS)0b0000648190889b
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQE48.8 _b.D68 1995eb
100	1		_aDowla, Farid U., _eauthor. _923854
245	1	0	_aSolving problems in environmental engineering and geosciences with artificial neural networks / _cFarid U. Dowla and Leah L. Rogers.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1995.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2003]
300			_a1 PDF (x, 239 pages) : _billustrations, maps.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
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) _93414
650		0	_aEnvironmental engineering _xData processing. _923855
650		0	_aEarth sciences _xData processing. _920187
655		0	_aElectronic books. _93294
700	1		_aRogers, Leah L. _923856
710	2		_aIEEE Xplore (Online Service), _edistributor. _923857
710	2		_aMIT Press, _epublisher. _923858
776	0	8	_iPrint version _z9780262515726
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6308075
942			_cEBK
999			_c73291 _d73291