ESTIMATING PEAK OUTFLOW OF EARTH FILL DAM FAILURES BY MULTIVARIABLE STATISTICAL MODELS

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NOORI M.; KHODASHENAS S.R.; REZAEI PAZHAND H.

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Journal: Journal of Water and Soil Year:2015 | Volume:29 | Issue:2 Page(s): 393-405

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Title

ESTIMATING PEAK OUTFLOW OF EARTH FILL DAM FAILURES BY MULTIVARIABLE STATISTICAL MODELS

Author(s)

NOORI M. | KHODASHENAS S.R. | REZAEI PAZHAND H. | Issue Writer Certificate

Keywords

DAM FAILUREQ2

DETERMINED COEFFICIENTQ2

DIAGNOSTICQ1

EFFICIENCYQ2

PEAK OUTFLOWQ2

Abstract

Introduction: DAM FAILURE and its flooding is one of the destructive phenomena today. Therefore, estimatingthe 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 breachbottom at failure (Vw), reservoir surface area (A), storage (S) and dam height (Hd). Various researchers haveproposed 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 newregression 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 ofwhich sufficient data are available for analysis. DAM FAILURE phenomenon is a rapidly varied unsteady flow that isexplained by shallow waters equations. The equations in the one-dimensional form are known as Saint-Venantequations and are based on hydrostatic pressure distribution and uniform flow under rectangular steepassumption. Although hydraulic methods to predict the DAM FAILURE flood have been developed by differentsoftware, due to the complex nature of the problem and the impossibility of considering all parameters inhydraulic analysis, statistical methods have been developed in this field. Statistical methods determine theequations that can approximate the required factors from the observed parameters. Multiple regression is a usefultechnique to model effective parameters in Qp, which can examine the statistical aspects of the model. This workis done by different tests, such as the model coefficients necessity test, analysis of variance table and it createsconfidence intervals. Data analysis in this paper is done by SPSS 16 software. This software can provide fitmodel, 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 theinitial 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 threeconvenient linear, multiplicative and transformed bases on Vw, Hw and Qp (nonlinear terms Qp). The best modelsfor each of the three models were selected. Their corrected determination coefficients (Adj R2) are close togetherand are between 0.86 until 0.864. The relative EFFICIENCY criteria based on the root mean square error (RMSE) was used to determine the best model. This standard was also used for other researchers’ models. RMSE of the three models presented in this article is lower than that of other models (from 745 to 759). DIAGNOSTICs analysis of the three models is not possible due to the large volume, so some statistical analysis for the model 2 arepresented in detail. The results are given in the following Tables. Test level has been assumed to be 5%. Fromthe point view of hydraulics, it can be said that the final equation for Qp should be proportional to Hw 1.5. Soalthough the model (2) has the lowest RMSE, but the model (3) of the hydraulics viewpoint seems more logicaland its RMSE is not very different from the model (2), so this model can be selected as the best model. Figure 1show DIAGNOSTICs diagrams of model (3). The right Figure shows the homogeneity of residuals (follow the normallaw) as a histogram. This homogeneity is confirmed by the crouch graph (center Figure). The left graph showsthe stabilization of residual variance. According to the preliminary and DIAGNOSTICs tests results, the model (3)has been selected. Its determination coefficient (0.864) also shows good strength.Conclusion: In this study, data from 140 broken dams were used to provide an appropriate model forestimating the PEAK OUTFLOW of DAM FAILURE. Standard statistical principles including preliminary tests, DIAGNOSTICand the EFFICIENCY of the models are the innovations of this paper. Analysis showed that the three models arecompetitive, and that the best of them was selected. The DETERMINED COEFFICIENT of these models was from 0.86 to0.864 ranges. Relative EFFICIENCY was calculated by the RMSE index. The results showed that these models aremore accurate than the models presented by other researchers. The model (3) was presented in this research, thebest result was estimated for Qp and its error was less than the other models.

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APA: Copy

NOORI, M., KHODASHENAS, S.R., & REZAEI PAZHAND, H.. (2015). ESTIMATING PEAK OUTFLOW OF EARTH FILL DAM FAILURES BY MULTIVARIABLE STATISTICAL MODELS. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 29(2), 393-405. SID. https://sid.ir/paper/141077/en

Vancouver: Copy

NOORI M., KHODASHENAS S.R., REZAEI PAZHAND H.. ESTIMATING PEAK OUTFLOW OF EARTH FILL DAM FAILURES BY MULTIVARIABLE STATISTICAL MODELS. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2015;29(2):393-405. Available from: https://sid.ir/paper/141077/en

IEEE: Copy

M. NOORI, S.R. KHODASHENAS, and H. REZAEI PAZHAND, “ESTIMATING PEAK OUTFLOW OF EARTH FILL DAM FAILURES BY MULTIVARIABLE STATISTICAL MODELS,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 29, no. 2, pp. 393–405, 2015, [Online]. Available: https://sid.ir/paper/141077/en

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