Prediction of Dam Breach Characteristics Using Multivariate Regression Analysis

Abstract Submission: Earthen dams play a critical role in mitigating flood hazards. However, due to climate change, the increased frequency and intensity of floods over the last decades have elevated the risk of dam failure through overtopping. Accurate prediction of dam breach characteristics, as well as their resulting impacts on the downstream floodplains, is needed for developing flood hazard countermeasures and emergency plans. In this study, multivariate regression analyses are used to predict dam breach characteristics using datasets generated from a numerical model developed in-house to simulate dam breach. Key predicted dam breach characteristics include maximum downstream water depths, time to peak breach flow, and breach widening and deepening rates. The dam parameters are the peak inflow discharge, tailwater depth, dam height and volume, upstream reservoir volume, and initial breach dimensions. The order of significance of each dam parameter on the dam breach characteristics is derived using quality measures, such as the adjusted R2, BIC, and RMSE. The best regression model for each breach characteristic is determined by backward elimination of dam parameters, starting with the least to the most significant parameter while observing the corresponding quality of the resulting model during each elimination step. The best regression models are presented, highlighting the most influential dam parameters for each breach characteristic. Finally, the integration of these regression models into the National Water Model is proposed for future work.

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