Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis

Accurate prediction of a breached dam's peak outflow is a significant factor for flood risk analysis. In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam peak o...

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Autores principales:	Mohammad Nobarinia, Farhoud Kalateh, Vahid Nourani, Alireza Babaeian Amini
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	empirical equation gene expression programming kernel extreme learning machine meta model peak outflow uncertainty Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/8528cb1616744a8b97cab1a231f3cfde
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spelling	oai:doaj.org-article:8528cb1616744a8b97cab1a231f3cfde2021-11-23T18:55:41ZDam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis1606-97491607-079810.2166/ws.2021.100https://doaj.org/article/8528cb1616744a8b97cab1a231f3cfde2021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3387https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Accurate prediction of a breached dam's peak outflow is a significant factor for flood risk analysis. In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam peak outflow prediction. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the models' accuracy up to 40%. The outcome of the applied models was also compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not have significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling. HIGHLIGHTS Some novelties of the present research can be summarized as follows: Applicability and accuracy of two different meta models, namely, Gene Expression Programming (GEP) and Support Vector Machine (SVM), are used to predict the peak outflow from breached embankment dams.; Two different scenarios are developed based on dam reservoir height and volume at the time of failure and soil characteristics.; Additional to the geometrical characteristics of dam, the impact of soil characteristics on modelling the peak outflow is investigated.; The outcomes of the SVM and GEP models are also compared with the existing empirical equations and it is shown that the intelligence models yield better results.;Mohammad NobariniaFarhoud KalatehVahid NouraniAlireza Babaeian AminiIWA Publishingarticleempirical equationgene expression programmingkernel extreme learning machinemeta modelpeak outflowuncertaintyWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3387-3401 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	empirical equation gene expression programming kernel extreme learning machine meta model peak outflow uncertainty Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	empirical equation gene expression programming kernel extreme learning machine meta model peak outflow uncertainty Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Mohammad Nobarinia Farhoud Kalateh Vahid Nourani Alireza Babaeian Amini Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis
description	Accurate prediction of a breached dam's peak outflow is a significant factor for flood risk analysis. In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam peak outflow prediction. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the models' accuracy up to 40%. The outcome of the applied models was also compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not have significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling. HIGHLIGHTS Some novelties of the present research can be summarized as follows: Applicability and accuracy of two different meta models, namely, Gene Expression Programming (GEP) and Support Vector Machine (SVM), are used to predict the peak outflow from breached embankment dams.; Two different scenarios are developed based on dam reservoir height and volume at the time of failure and soil characteristics.; Additional to the geometrical characteristics of dam, the impact of soil characteristics on modelling the peak outflow is investigated.; The outcomes of the SVM and GEP models are also compared with the existing empirical equations and it is shown that the intelligence models yield better results.;
format	article
author	Mohammad Nobarinia Farhoud Kalateh Vahid Nourani Alireza Babaeian Amini
author_facet	Mohammad Nobarinia Farhoud Kalateh Vahid Nourani Alireza Babaeian Amini
author_sort	Mohammad Nobarinia
title	Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis
title_short	Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis
title_full	Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis
title_fullStr	Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis
title_full_unstemmed	Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis
title_sort	dam failure peak outflow prediction through gep-svm meta models and uncertainty analysis
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/8528cb1616744a8b97cab1a231f3cfde
work_keys_str_mv	AT mohammadnobarinia damfailurepeakoutflowpredictionthroughgepsvmmetamodelsanduncertaintyanalysis AT farhoudkalateh damfailurepeakoutflowpredictionthroughgepsvmmetamodelsanduncertaintyanalysis AT vahidnourani damfailurepeakoutflowpredictionthroughgepsvmmetamodelsanduncertaintyanalysis AT alirezababaeianamini damfailurepeakoutflowpredictionthroughgepsvmmetamodelsanduncertaintyanalysis
_version_	1718416162372452352

Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis

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