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HYDROLOGICAL MODELING OF WATER FLOW IN THE RIVER USING ARTIFICIAL NEURAL NETWORK
Abstract
Modeling was carried out on the basis of hydrological data from the ?yna River (north-eastern Poland). The length of the river is 264 km, the average river slope is 1.2 ?, the catchment area is 7126 km2. Modeling was based on data on water flows and the amount of precipitation. Neural models were created in which the input data is the amount of rainfall and the output is the flow. The possessed data has been divided into two parts, one of which was used to teach and test ANN, and the other - smaller - to verify the obtained model. The several kinds of artificial neural network were tested (MLP, RBF) with different topology (the different number of neurons in the hidden layers). Five the best models were used for prediction (with the lowest learning and testing error). These five best networks were then used to predict flow rates depending on the amount of rainfall for the verification data set. On this basis, the error of individual neural models was estimated. At the same time, historical data on the volume of electricity production depending on the river water flow were used basis on the data from the hydroelectric power plants located on the ?yna River. This allows the use of Artificial Neural Network models to forecast not only the volume of flows depending on the amount of precipitation, but also the volume of electricity production in small hydropower plants on the ?yna River.
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