Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks
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| Title: | Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks |
|---|---|
| Description: | 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.Artificial 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. |
| Authors: | Farid U. Dowla, Leah L. Rogers |
| Resource Type: | eBook. |
| Subjects: | Earth sciences--Data processing, Environmental engineering--Data processing, Neural networks (Computer science) |
| Categories: | SCIENCE / Earth Sciences / General |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks – Name: Abstract Label: Description Group: Ab Data: 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.Artificial 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farid+U%2E+Dowla%22">Farid U. Dowla</searchLink><br /><searchLink fieldCode="AR" term="%22Leah+L%2E+Rogers%22">Leah L. Rogers</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Earth+sciences--Data+processing%22">Earth sciences--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+engineering--Data+processing%22">Environmental engineering--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+networks+%28Computer+science%29%22">Neural networks (Computer science)</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22SCIENCE+%2F+Earth+Sciences+%2F+General%22">SCIENCE / Earth Sciences / General</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 550.285 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Earth sciences--Data processing Type: general – SubjectFull: Environmental engineering--Data processing Type: general – SubjectFull: Neural networks (Computer science) Type: general Titles: – TitleFull: Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farid U. Dowla – PersonEntity: Name: NameFull: Leah L. Rogers – PersonEntity: Name: NameFull: Farid U. Dowla – PersonEntity: Name: NameFull: Leah L. Rogers IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 1995 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9780262041485 – Type: isbn-electronic Value: 9780262271912 Titles: – TitleFull: Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks Type: main |
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