engleski

A Systematic Approach to a Time Series Neural Model Development for River Flow Forecasting

This paper aims to apply a systematic approach to a time series neural model development procedure. The model is developed for flow forecasting of river Cetina, techno- economically the most important basin in Croatia according to the annual energy production. Multi-Layer Perceptron was used to model hydrological time series of a measured daily river flow. The best model was determined through an experiment based on a values comparison of different error measures (SEE, RMSE, MAE, and CE). In order to determine the best model, 780 MLP neural networks were trained using Levenberg-Marquardt training algorithm. Simulation results indicate high accuracy of flow forecast for one-step-ahead and therefore provide encouragement for further research.

Artificial Neural Networks ; Forecasting ; Modeling ; River Flow ; Time Series

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