Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks |
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Authors: | Abdüsselam Altunkaynak |
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Affiliation: | (1) Faculty of Civil Engineering, Hydraulics Division, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey |
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Abstract: | Lake Van in eastern Turkey has been subject to water level rise during the last decade and, consequently, the low-lying areas
along the shore are inundated, giving problems to local administrators, governmental officials, irrigation activities and
to people's property. Therefore, forecasting water levels of the Lake has started to attract the attention of the researchers
in the country. An attempt has been made to use artificial neural networks (ANN) for modeling the temporal change water levels
of Lake Van. A back-propagation algorithm is used for training. The study indicated that neural networks can successfully
model the complex relationship between the rainfall and consecutive water levels. Three different cases were considered with
the network trained for different arrangements of input nodes, such as current and antecedent lake levels, rainfall amounts.
All of the three models yields relatively close results to each other. The neural network model is simpler and more reliable
than the conventional methods such as autoregressive (AR), moving average (MA), and autoregressive moving average with exogenous
input (ARMAX) models. It is shown that the relative errors for these two different models, are below 10% which is acceptable
for engineering studies. In this study, dynamic changes of the lake level are evaluated. In contrast to classical methods,
ANNs do not require strict assumptions such as linearity, normality, homoscadacity etc. |
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Keywords: | Hydrologic budget Lake level Neural networks Prediction |
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