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Title: Multi-objective optimal design for flood risk management with resilience objectives
Authors: Su, Hsin-Ting
Cheung, Sai Hung
Lo, Edmond Yat-Man
Keywords: Engineering::Civil engineering
Issue Date: 2018
Source: Su, H.-T., Cheung, S. H., & Lo, E. Y.-M. (2018). Multi-objective optimal design for flood risk management with resilience objectives. Stochastic Environmental Research and Risk Assessment, 32(4), 1147-1162. doi:10.1007/s00477-017-1508-7
Journal: Stochastic Environmental Research and Risk Assessment
Abstract: In flood risk management, the divergent concept of resilience of a flood defense system cannot be fully defined quantitatively by one indicator and multiple indicators need to be considered simultaneously. In this paper, a multi-objective optimization (MOO) design framework is developed to determine the optimal protection level of a levee system based on different resilience indicators that depend on the probabilistic features of the flood damage cost arising under the uncertain nature of rainfalls. An evolutionary-based MOO algorithm is used to find a set of non-dominated solutions, known as Pareto optimal solutions for the optimal protection level. The objective functions, specifically resilience indicators of severity, variability and graduality, that account for the uncertainty of rainfall can be evaluated by stochastic sampling of rainfall amount together with the model simulations of incurred flood damage estimation for the levee system. However, these model simulations which usually require detailed flood inundation simulation are computationally demanding. This hinders the wide application of MOO in flood risk management and is circumvented here via a surrogate flood damage modeling technique that is integrated into the MOO algorithm. The proposed optimal design framework is applied to a levee system in a central basin of flood-prone Jakarta, Indonesia. The results suggest that the proposed framework enables the application of MOO with resilience objectives for flood defense system design under uncertainty and solves the decision making problems efficiently by drastically reducing the required computational time.
ISSN: 1436-3240
DOI: 10.1007/s00477-017-1508-7
Rights: © 2018 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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