Water temperature is an important parameter that influences the life near watercourses. The physical and chemical properties as the density and viscosity, gas solubility in water and in particular of the oxygen, the kinematics of the micro-biological and chemical phenomena affects not only the aquatic life but also the human life and animals living near the watercourse. In the Jiu river is discharged the water coming from the cooling towers of three thermo-electric power plants placed upstream. According to the recent researches realized by the EU Environmental Commission in Romania until 2050 the average temperature could rise by about 2°C. This paper presents the ability of a “neural networks” model to predict the water temperature in the lower part of the Jiu River. A database realized in over nine years in the measuring station Izbiceni, from the Olt County is used, concerning the water temperature (Tw) and the air temperature (Ta). The developed neural network is structured on five input variables and 9 hidden layers, 7 as sub-input and 2 as output. The stochastic time series is based on the separation of values for water Tw and air Ta in seasonal and daily fluctuations components. The schematic structure of the implemented neural network is presented, for obtaining the short-term and the seasonal components of the temperatures. The contact points are adjusted by the method of reprogramming after a gradient algorithm, in order to minimize the quadratic error between the output results and the relevant observed values. The performance of the neural network is based on estimation the thermal changes of the operation mode. For testing the numerical model, the calculations were performed using only the data recorded in nine years, in order to estimate a forecast for the next two years. The results are very close to the experimental recorded data and the obtained errors are less than 0.5°C. At the end of the paper are presented some results, conclusions and references.

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