Abstract
Predviđanje maksimalnih godišnjih poplavnih protoka primjenom umjetnih neuronskih mreža
Highlights
In Turkey, flooding is a highly important natural hazard, second only to earthquakes
backpropagation learning algorithm (BP) and Conjugate Gradient (CG) learning algorithms were examined by determining the mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE) values for both validation and test data
The type of transfer function has a great effect on the performance of the artificial neural networks (ANN) model
Summary
In Turkey, flooding is a highly important natural hazard, second only to earthquakes. Different quantile estimation studies based on regression models [17,18,19,20,21,22] have clearly indicated that regression based methods of flood regionalization are reliable for flood discharge estimation using variables dependent on site characteristics at ungauged sites. Aziz et al [28] examined the utility of the ANN based regional flood frequency analysis (RFFA) method and compared the performances of the ANNbased RFFA models with regression analysis. Seckin et al [29] developed ANN, linear and nonlinear models as alternatives to L-moments method for estimation of flood peaks of various return periods. They showed that the estimator productivity of the ANN multi-layer perceptrons model led to a much better performance than others. The performance of each method is evaluated by the mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and relative error (RE) values
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