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Ecological Informatics: Scope, Techniques and Applications

By: Friedrich Recknagel(Editor)

496 pages, illustrations


Paperback | Nov 2014 | Edition: 2 | #224183 | ISBN-13: 9783642446771
Availability: Usually dispatched within 1 week Details
NHBS Price: £207.50 $273/€237 approx
Hardback | Dec 2006 | Edition: 2 | #156260 | ISBN: 3540283838
Availability: Usually dispatched within 1 week Details
NHBS Price: £199.99 $264/€228 approx

About this book

Ecological Informatics: Understanding Ecology by Biologically-Inspired Computation promotes interdisciplinary research between ecology and computer science on elucidation of principles of information processing in ecosystems, ecological sustainability by informed decision making, and bio-inspired computation. The 2nd edition of the book consolidates the scope, concepts, and techniques of this newly emerging discipline by a new preface and additional chapters on cellular automata, qualitative reasoning, hybrid evolutionary algorithms and artificial neural networks. It illustrates numerous applications of Ecological Informatics for aquatic and terrestrial ecosystems, image recognition at micro- and macro-scale as well as computer hardware design.

Case studies focus on applications of artificial neural networks, evolutionary computation, cellular automata, adaptive agents, fuzzy logic as well as qualitative reasoning.

The 2nd edition of Ecological Informatics: Understanding Ecology by Biologically-Inspired Computation includes an index with novel evolutionary algorithms for the discovery of multiple nonlinear functions and rule sets as well as parameter optimisation in complex ecological data.


- Artificial neural networks
- Fuzzy logic
- Adaptive agents
- Predictive modeling and simulation
- Knowledge discovery
- Sensitivity analysis
- Principal component analysis
- Multiple nonlinear regression
- Time series analysis
- Bioassessment of freshwater habitats
- Forecasting of toxic algal blooms
- Sustainable fisheries
- Conservation of biodiversity
- Automated classification of plankton
- Automated classification of remotely sensed landscapes

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