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A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam

This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam ove...

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Main Authors: Sharafati, Ahmad, Yaseen, Zaher Mundher, Shahid, Shamsuddin
Format: Article
Language:English
Published: John Wiley & Sons, Inc. 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/94125/1/ShamsuddinShahid2021_ANovelSimulationOptimizationStrategy.pdf
http://eprints.utm.my/id/eprint/94125/
http://dx.doi.org/10.1111/jfr3.12678
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spelling my.utm.941252022-02-28T13:32:22Z http://eprints.utm.my/id/eprint/94125/ A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam Sharafati, Ahmad Yaseen, Zaher Mundher Shahid, Shamsuddin TA Engineering (General). Civil engineering (General) This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam overtopping probability, as those are the two major concerns in designing flood control dams. The nondominated solutions are obtained through a multi-objective particle swarm optimization (MOPSO) approach. Results indicate that stochastic sources have a significant impact on Pareto front solutions. The distance index (DI) reveals the rainfall depth (DI = 0.41) as the most significant factor affecting the Pareto front and the hydraulic parameters (DI = 0.02) as the least. The dam overtopping probability is found to have a higher sensitivity to the variability of stochastic sources compared to annual cost of dam implementation. The values of interquartile range (IQR) indicate that the dam overtopping probability is least uncertain when all stochastic sources are considered (IQR = 0.25%). The minimum annual cost of dam implementation (2.79 M$) is also achieved when all stochastic sources are considered in optimization process. The results indicate the potential of the proposed method to be used for better designing of flood control dam through incorporation of all sources of uncertainty. John Wiley & Sons, Inc. 2021-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94125/1/ShamsuddinShahid2021_ANovelSimulationOptimizationStrategy.pdf Sharafati, Ahmad and Yaseen, Zaher Mundher and Shahid, Shamsuddin (2021) A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam. Journal of Flood Risk Management, 14 (1). pp. 1-19. ISSN 1753-318X http://dx.doi.org/10.1111/jfr3.12678 DOI:10.1111/jfr3.12678
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Sharafati, Ahmad
Yaseen, Zaher Mundher
Shahid, Shamsuddin
A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam
description This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam overtopping probability, as those are the two major concerns in designing flood control dams. The nondominated solutions are obtained through a multi-objective particle swarm optimization (MOPSO) approach. Results indicate that stochastic sources have a significant impact on Pareto front solutions. The distance index (DI) reveals the rainfall depth (DI = 0.41) as the most significant factor affecting the Pareto front and the hydraulic parameters (DI = 0.02) as the least. The dam overtopping probability is found to have a higher sensitivity to the variability of stochastic sources compared to annual cost of dam implementation. The values of interquartile range (IQR) indicate that the dam overtopping probability is least uncertain when all stochastic sources are considered (IQR = 0.25%). The minimum annual cost of dam implementation (2.79 M$) is also achieved when all stochastic sources are considered in optimization process. The results indicate the potential of the proposed method to be used for better designing of flood control dam through incorporation of all sources of uncertainty.
format Article
author Sharafati, Ahmad
Yaseen, Zaher Mundher
Shahid, Shamsuddin
author_facet Sharafati, Ahmad
Yaseen, Zaher Mundher
Shahid, Shamsuddin
author_sort Sharafati, Ahmad
title A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam
title_short A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam
title_full A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam
title_fullStr A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam
title_full_unstemmed A novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of Jamishan dam
title_sort novel simulation-optimization strategy for stochastic-based designing of flood control dam: a case study of jamishan dam
publisher John Wiley & Sons, Inc.
publishDate 2021
url http://eprints.utm.my/id/eprint/94125/1/ShamsuddinShahid2021_ANovelSimulationOptimizationStrategy.pdf
http://eprints.utm.my/id/eprint/94125/
http://dx.doi.org/10.1111/jfr3.12678
_version_ 1726791484909289472
score 13.211869

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