Comparing the Performance of Neural Networks for Predicting Peak Outflow from Breached Embankments when Back Propagation Algorithms Meet Evolutionary Algorithms

This investigation provides a review of some methods for estimation of peak outflow from breached dams and presents an effective and efficient model for predicting peak outflow based on artificial neural network (ANN). For this reason the case study data on peak outflow discharge were compiled from various sources and reanalyzed using the ANN technique to see if better predictions are possible. By employing two important effective parameters namely, height (Hw) and volume (Vw) of water behind the dam at failure, four scenarios were addressed. To train the models two different algorithms were examined namely, back propagation (BP) and imperialist competitive algorithm (ICA). Among the BP algorithms, Levenberg–Marquardt (LM), resilient back propagation (RP), fletcher–reeves update (CGF), and scaled conjugate gradient (SCG) were utilized. Therefore, 20 different ANN models were developed and compared to each other. Results showed that both Hw and Vw parameters are similarly dominant in estimating the peak outflow discharge. Among the different training functions, LM was the best, because of higher coefficient of determination (R2=0.87) and lower error (RMSE=9616). Comparing the results of ANN and empirical formulas indicated higher ANN performance, so such formulas are better to be replaced with superior ANN model.