The Têt, main river of the Pyrénées-Orientales department (south of France) has a significant impact on the life of the department. The management of its water quality must be largely improved and better monitored. In this sense, the present work takes part in a global development and evaluation of reliable and robust tools, with the aim of allowing the control and supervision of the Têt River's lowland area. A simplified model, based on mass balances, has been developed to estimate nutrient levels in the stream and to describe the river water quality. Due to, the application of mathematical models for river water quality as support tools is often limited by the availability of reliable data, Kohonen self-organizing maps were used to solve it and to avoid the data missing. This kind of neural networks proved to be very useful to predict missing components and to complete the available database, describing the chemical state of the river and the WWTPs operation.
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