Estimation of Rainfall-Runoff Relationship Using Artificial Neural Network Models for Muskegon Basin

Fatih ÜNEŞ, Onur BÖLÜK, Yunus Ziya KAYA, Bestami TAŞAR, Hakan VARÇİN

Rainfall – runoff relation, Artificial neural networks, Multiple linear regression, Prediction.

In order to determine the use, protection and economic life of water resources; it is important to make estimations about rainfall-runoff values. However, it is quite complicated to estimate rainfall-runoff. For this reason, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods, which are widely used today for complex hydrological problems, are preferred for the rainfall-runoff model. For model creation, the hydrological and seasonal data from the United States Muskegon basin are used. Estimation study was done with ANN and MLR methods using 1396 daily rainfall, temperature and rainfall data belonging to the region. According to the model results, it is seen that the ANN method has results with low error and high determination in the rainfall runoff model. ANN method can be used as an alternative way to classical methods in rainfall-runoff predictions.

10.22161/ijaers.5.12.28

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