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

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Index Numbers and Productivity Measurement,s for resources of drinking, agriculture, industry, energy and recreation, has been increasing rapidly due to the problems of water shortages and pollutions. Especially stream ecosystems flow through stressful sources, and are exposed to various natural and anthropogenic disturbances. Due to unique

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Aphra Behn’s , and Daniel Defoe’s ngly influence the distribution and abundance of lotic biota, and thus the composition of macroinvertebrate assemblages (e.g. Hynes 1970). Many studies have identified substrate composition, complexity and heterogeneity as major determinants of in-stream biota (e.g. Downes et al. 1998). Other abioti

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https://doi.org/10.1007/978-1-349-16482-0ns and predict future ecosystem behaviors (Odum 1983; Krebs 1994). For accomplishing both objectives, it is necessary to use models that adequately address the uncertainty and complexity of ecosystems. Artificial Neural Networks (ANN) have been demonstrated to successfully model non-linear and compl

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Environmental Biotechnology of Fossil Fuels,mitation of recreational use, depletion of dissolved oxygen levels below tolerable levels for certain fish species and severe ecosystem degradation are amongst the adverse effects of eutrophication (Ryding and Rast 1989).

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