Estimating Peak Outflow of Earth Fill Dam Failures by Multivariable Statistical Models

Introduction: Dam failure and its flooding is one of the destructive phenomena today. Therefore, estimating the peak outflow (QP) with reasonable accuracy and determining the related flood zone can reduce risks. Qp of dam failure depends on important factors such as: depth above breach (Hw), volume of water above breach bottom at failure (Vw), reservoir surface area (A), storage (S) and dam height (Hd). Various researchers have proposed equations to estimate QP. They used the regression method to obtain an appropriate equation. Regression is a mathematical technique that requires initial test and diagnosis. These researchers present a new regression model for a better estimation of Qp. Materials and Methods: The data used in this study are related to 140 broken dams in the world for 34 of which sufficient data are available for analysis. Dam failure phenomenon is a rapidly varied unsteady flow that is explained by shallow waters equations. The equations in the one-dimensional form are known as Saint-Venant equations and are based on hydrostatic pressure distribution and uniform flow under rectangular steep assumption. Although hydraulic methods to predict the dam failure flood have been developed by different software, due to the complex nature of the problem and the impossibility of considering all parameters in hydraulic analysis, statistical methods have been developed in this field. Statistical methods determine the equations that can approximate the required factors from the observed parameters. Multiple regression is a useful technique to model effective parameters in Qp, which can examine the statistical aspects of the model. This work is done by different tests, such as the model coefficients necessity test, analysis of variance table and it creates confidence intervals. Data analysis in this paper is done by SPSS 16 software. This software can provide fit model, various characteristics and related tests in the Tables. Results and Discussion:This paper proposes a new relationship with better estimation of discharge peak (Qp) based on Hw and Vw factors. Results showed how to choose the appropriate model (fitting the model) and the initial required tests, according to the diagnostic model. And it compares the estimated error (relative efficiency) of the researchers’ models with the proposed models. The number of models can be classified to three convenient linear, multiplicative and transformed bases on Vw, Hw and Qp (nonlinear terms Qp). The best mo