Improving Prediction of Dam Failure Peak Outflow Using Neuroevolution Combined with K-Means Clustering

Estimation of peak outflow resulting from dam failure is of paramount importance for flood risk analysis. This paper presents a new hybrid clustering model based on artificial neural networks and genetic algorithms (ANN-GA) for improving predictions of peak outflow from breached embankment dams. The input layer of the ANN-based model comprises height and volume of water behind the breach at failure time plus a new parameter of cluster number. The cluster number is obtained from partitioning the input data set using the k-means clustering technique. The model is demonstrated using the data samples collected from the literature and compared with three benchmark models by using a cross-validation method. The benchmark models consist of a conventional regression model and two ANN models trained by nonlinear techniques. Results indicate that the suggested model is able to estimate the peak outflows more accurately, especially for big flood events. The best prediction for the current database was obtained from a five-clustered ANN-GA model. The uncertainty analysis shows the five-clustered ANN-GA model has the lowest prediction error and the smallest uncertainty. (C) 2017 American Society of Civil Engineers.