Modeling of River Water Temperatures using Feed-forward Artificial Neural Networks

Water temperature influences most physical, chemical and biological processes of the river environment. It plays an important role in the distribution of fishes and on the growth rates of many aquatic organisms. It is therefore important to develop water temperature models in order to effectively manage aquatic habitats, to study the thermal regime of rivers and to have effective tools for environmental impact studies. The objective of the present study was to develop a water temperature model based on artificial neural networks (ANN) for two thermally different watercourses. The ANN model performed best in summer and autumn and showed a poorer (but still good) performance in spring. The many advantages of ANN models are their simplicity, low data requirements, their capability of modelling long-term series as well as have an overall

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