Recknagel Ecological Informatics

Understanding Ecology by Biologically-Inspired Computation
Erscheinungsjahr 2013
ISBN: 978-3-662-05150-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 398 Seiten, eBook

Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Recknagel, Friedrich

Inhaltsverzeichnis

I Introduction.- 1. Ecological Applications of Fuzzy Logic.- 2. Ecological Applications of Unsupervised Artificial Neural Networks.- 3. Ecological Applications of Genetic Algorithms.- 4. Ecological Applications of Evolutionary Computation.- 5. Ecological Applications of Adaptive Agents.- II Prediction and Elucidation of Stream Ecosystems.- 6. Development and Application of Predictive River Ecosystem Models Based On Classification Trees and Artificial Neural Networks.- 7. Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- 8. Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- 9. Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- III Prediction and Elucidation of River Ecosystems.- 10. Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- 11. An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- 12. Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- IV Prediction and Elucidation of Lake and Marine Ecosystems.- 13. A Comparison between Neural Network Based and Multiple Regression Models in Chlorophyll-a Estimation.- 14. A Generic Artificial Neural Network Model for Dynamic Predictions of Algal Abundance in Freshwater Lakes.- 15. Predictive Rules for Phytoplankton Dynamics in Freshwater Lakes Discovered by Evolutionary Algorithms.- 16. Multivariate Time-Series Prediction of Marine Zooplankton by Artificial Neural Networks.- 17. Classification of Fish Stock-Recruitment Relationships in Different Environmental regimes by Fuzzy Logic Combined with a Bootstrap Re-sampling Approach.- V Classification of Ecological Images at Micro and Macro Scale.- 18. Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- 19. Age Estimation of Fish Using a Probabilistic Neural Network.- 20. Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.