On comparison of two-level and global optimization schemes for layout design of storage ponds

Optimization techniques have emerged as robust tools to aid the planning and design of urban drainage facilities in cost-effective ways. Such an effort was traditionally aided by heuristic methods (like genetic algorithm), which was generally time-consuming and also challenging in reaching convergen...

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Main Authors: Lu, Wei, Qin, Xiaosheng, Yu, Jianjun
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/151238
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1512382021-06-22T03:05:55Z On comparison of two-level and global optimization schemes for layout design of storage ponds Lu, Wei Qin, Xiaosheng Yu, Jianjun School of Civil and Environmental Engineering Environmental Process Modelling Centre Nanyang Environment and Water Research Institute Engineering::Environmental engineering Urban Drainage System Storage Pond Optimization techniques have emerged as robust tools to aid the planning and design of urban drainage facilities in cost-effective ways. Such an effort was traditionally aided by heuristic methods (like genetic algorithm), which was generally time-consuming and also challenging in reaching convergence for large-scale problems with wide decision spaces. This study proposed a novel optimization method, denoted as two-level optimization (TO) scheme, for supporting rainwater storage pond design in an urban drainage system. Polynomial regression models were established as surrogate models to facilitate the solution of the optimization framework using traditional iteration algorithm. The TO scheme firstly sought the optimal layout of storage ponds on tributary sub-watersheds, and then proceeded to that of the mainstream one to yield the final solution. Through a case study, the TO scheme was compared with the traditional global optimization (GO) scheme where the physical simulation model was dynamically linked with genetic algorithm (GA) to seek the global optimal solution. The performance of two schemes under different constraint settings was analyzed. Effects of related issues such as start-point selection and mainstream design on tributary sub-watersheds were also discussed. The results showed that the proposed TO scheme is a prominent alternative to the traditional GO scheme to support urban water managers for a more science-based decision making towards storage pond implementation in large-scale practical problems. Ministry of Education (MOE) The research work was supported by Singapore’s Ministry of Education (MOE) AcRF Tier 1 Project (Ref No. RG170/16; WBS No.: 4011766.030). 2021-06-22T03:05:55Z 2021-06-22T03:05:55Z 2019 Journal Article Lu, W., Qin, X. & Yu, J. (2019). On comparison of two-level and global optimization schemes for layout design of storage ponds. Journal of Hydrology, 570, 544-554. https://dx.doi.org/10.1016/j.jhydrol.2018.10.078 0022-1694 0000-0003-3187-7561 https://hdl.handle.net/10356/151238 10.1016/j.jhydrol.2018.10.078 2-s2.0-85060522896 570 544 554 en RG170/16 4011766.030 Journal of Hydrology © 2019 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Environmental engineering
Urban Drainage System
Storage Pond
spellingShingle Engineering::Environmental engineering
Urban Drainage System
Storage Pond
Lu, Wei
Qin, Xiaosheng
Yu, Jianjun
On comparison of two-level and global optimization schemes for layout design of storage ponds
description Optimization techniques have emerged as robust tools to aid the planning and design of urban drainage facilities in cost-effective ways. Such an effort was traditionally aided by heuristic methods (like genetic algorithm), which was generally time-consuming and also challenging in reaching convergence for large-scale problems with wide decision spaces. This study proposed a novel optimization method, denoted as two-level optimization (TO) scheme, for supporting rainwater storage pond design in an urban drainage system. Polynomial regression models were established as surrogate models to facilitate the solution of the optimization framework using traditional iteration algorithm. The TO scheme firstly sought the optimal layout of storage ponds on tributary sub-watersheds, and then proceeded to that of the mainstream one to yield the final solution. Through a case study, the TO scheme was compared with the traditional global optimization (GO) scheme where the physical simulation model was dynamically linked with genetic algorithm (GA) to seek the global optimal solution. The performance of two schemes under different constraint settings was analyzed. Effects of related issues such as start-point selection and mainstream design on tributary sub-watersheds were also discussed. The results showed that the proposed TO scheme is a prominent alternative to the traditional GO scheme to support urban water managers for a more science-based decision making towards storage pond implementation in large-scale practical problems.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Lu, Wei
Qin, Xiaosheng
Yu, Jianjun
format Article
author Lu, Wei
Qin, Xiaosheng
Yu, Jianjun
author_sort Lu, Wei
title On comparison of two-level and global optimization schemes for layout design of storage ponds
title_short On comparison of two-level and global optimization schemes for layout design of storage ponds
title_full On comparison of two-level and global optimization schemes for layout design of storage ponds
title_fullStr On comparison of two-level and global optimization schemes for layout design of storage ponds
title_full_unstemmed On comparison of two-level and global optimization schemes for layout design of storage ponds
title_sort on comparison of two-level and global optimization schemes for layout design of storage ponds
publishDate 2021
url https://hdl.handle.net/10356/151238
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