International Journal of Hydraulic Engineering 2012, 1(6): 55-67 DOI: 10.5923/j.ijhe.20120106.01 Comparing the Performance of Neural Networks for Predicting Peak Outflow from Breached Embankments when Back Propagation Algorithms Meet Evolutionary Algorithms Farhad Hoos hyaripor, Ahmad Tahe rs hams i * Faculty of Civil and Environment Engineering, Amirkabir University of Technology, Tehran, Iran Abstract 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 (H w ) and volume (V w ) 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 H w and V w 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 (R 2 =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. Keywords Dam Breach, Peak Outflow Discharge, Neural Network, Training Algorithm 1. Introduction Dam failure is a catastrophic phenomenon that can lead to large damages to human life and property. Overviewing of historical dam failures shows that overtopping and piping were the major causes of dam failures. Overtopping is mostly dangerous for embankment dams because it washes away or erodes very quickly the dam’s materials. In Piping, water seeps under the dam and gradually erodes the dam materials. The extension of this phenomenon may lead to dam collapse. The various modes of breach formation in embankment dams, and the large number of factors that influence the outflow characteristics, are difficult to describe with rigorously precise mathematical formulas. Because of complexity and uncertainty resulting from the wide range of values of the effective parameters, it is worthwhile to reduce the mathematical complexity of the problem and to present simple methods to predict the outflow characteristics from breached embankment. Prediction of peak outflow is very important because of the emergency action plan preparation * Corresponding author: tshamsi@aut.ac.ir(Ahmad Tahershamsi) Published online at http://journal.sapub.org/ijhe Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved and risk assessment. In some former investigations, case study data were used to develop empirical formula by relating peak outflow to the height of water behind the dam and/or volume of water behind the dam. Some investigators developed single-variable equations[13],[21],[36-38],[42], and some others presented multi-variable equations[10], [16],[30],[31],[44]. Hagen[17] introduced “dam factor” as the product of height of water and reservoir storage volume at the time of failure, and proposed a formula relating the peak-breach outflow to the dam factor. Some investigators applied the dam factor in their proposed equations[11],[25]. Although applying empirical equations based on statistical regression is simple in practice, they are unable to estimate the values of peak outflow accurately. It is felt that this is partly due to the complexity of the phenomenon involved and low accuracy of data driven from historical dam failures[14], and partly because of the limitation of the analytical tool commonly used by most of the earlier investigators namely, traditional statistical regression. Nowadays, traditional statistical analysis has been replaced by newly alternative approaches in many cases. Artificial neural networks (ANN) as an alternative approach have advantages over statistical models like their data-driven nature, model–free form of predictions, and tolerance to data errors[4]. ANN beside its simplicity and generalizing ability