Vesna Rankovic

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The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based(More)
The paper presents the application of support vector regression (SVR) to accurate forecasting of the tangential displacement of a concrete dam. The SVR nonlinear autoregressive model with exogenous inputs (NARX) was developed and tested using experimental data collected during fourteen years. A total of 573 data were used for training of the SVR model(More)
Nonlinear system identification via Feedforward Neural Networks (FNN) and Digital Recurrent Network (DRN) is studied in this paper. The standard backpropagation algorithm is used to train the FNN. A dynamic backpropagation algorithm is employed to adapt weights and biases of the DRN. The neural networks are trained using the identified error between the(More)
Ljiljana C ̌ omićABSTRACT Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its(More)
Keywords: Feedforward neural network Gas flow Porous wall Velocity a b s t r a c t This paper concerns the use of feedforward neural networks (FNN) for predicting the nondimensional velocity of the gas that flows along a porous wall. The numerical solution of partial differential equations that govern the fluid flow is applied for training and testing the(More)