Suitability of Artificial Neural Network in Daily Flow Forecasting
This study aims to development of the Kasilian indicator
river flow forecasting system using Artificial Neural Network (ANN). In
this study the performance of multi-layer perceptrons or MLPs, the most
frequently used artificial neural network algorithm in the water resources
literature, in daily flow estimation and forecasting was investigated.
Kasilian watershed in Northern Iran, representing a continuous rain-fall
with a predictable stream flow events. Division of yearly data into four
seasons and development of separate networks accordingly was found to
be more useful than a single network applicable for the entire year. The
used data in ANN was hydrometric and climatic daily data with 10 years
duration from 1991 to 2000. For the mentioned model 8 years data were
used for its development but for the validation/testing of the model 2
years data was applied. Based on the results, the L-M algorithm is more
efficient than the CG algorithm, so it is used to train 6 ANNs models
for rain fall-runoff prediction at time step t+1 from time step t input.
The used network in this study was MLP with BP (back propagation) algorithm.
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