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 Front Matter....Pages I-XXVIII Front Matter....Pages 1-1 Ecological Applications of Fuzzy Logic....Pages 3-14 Ecological Applications of Unsupervised Artificial Neural Networks....Pages 15-33 Ecological Applications of Genetic Algorithms....Pages 35-48 Ecological Applications of Evolutionary Computation....Pages 49-71 Ecological Applications of Adaptive Agents....Pages 73-88 Front Matter....Pages 89-89 Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks....Pages 91-107 Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks....Pages 109-126 Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks....Pages 127-178 Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks....Pages 179-192 Front Matter....Pages 193-193 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....Pages 195-213 An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model....Pages 215-232 Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species....Pages 233-246 Front Matter....Pages 247-247 A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation....Pages 249-263 A Generic Artificial Neural Network Model for Short Term Predictions of Algal Blooms in Lakes and Reservoirs....Pages 265-289 Predictive Rules for Phytoplankton Dynamics in Freshwater Lakes Discovered by Evolutionary Algorithms....Pages 291-311 Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks....Pages 313-327 Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach....Pages 329-351 Front Matter....Pages 353-353 Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes....Pages 355-367 Age Estimation of Fish Using a Probabilistic Neural Network....Pages 369-382 Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks....Pages 383-398
