Titlebook: Ecological Informatics; Understanding Ecolog Friedrich Recknagel Book 20031st edition Springer-Verlag Berlin Heidelberg 2003 Artificial Int

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FID

The Use of Mortality Statistics,useful decision making tools, they are limited in terms of their ability to predict the timing, magnitude and algal species of significant bloom events (Recknagel et al. 1997; Recknagel and Wilson 2000).

夸张

Anhydrous

A Generic Artificial Neural Network Model for Short Term Predictions of Algal Blooms in Lakes and Reuseful decision making tools, they are limited in terms of their ability to predict the timing, magnitude and algal species of significant bloom events (Recknagel et al. 1997; Recknagel and Wilson 2000).

CRAB

拖债

Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networksd biological processes, and species-habitat interrelations are still insufficient. An integrative and prognostic ecological assessment of running waters thus is presently not available (e.g. Bayerisches Landesamt für Wasserwirtschaft 1998; Resh et al. 1994; Statzner et al. 1994; Townsend and Hildrew 1994; Townsend 1989; Vannote et al. 1980).

CRATE

Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblags have identified substrate composition, complexity and heterogeneity as major determinants of in-stream biota (e.g. Downes et al. 1998). Other abiotic factors such as flow velocity (e.g. Barmuta 1990) and water chemistry (e.g. Bunn et al. 1986) have also been found to influence biotic composition.

itinerary

Predictive Rules for Phytoplankton Dynamics in Freshwater Lakes Discovered by Evolutionary Algorithmnutrients. Blooms of blue-green algae can contaminate freshwater by toxins, anoxia, taste and odour causing high treatment costs in water works, risks for public health and degradation of freshwater ecosystems.

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