pith. machine review for the scientific record. sign in

arxiv: 2604.18620 · v1 · submitted 2026-04-17 · 💻 cs.NE

Recognition: unknown

Optimising Urban Flood Resilience

Christos Iliadis, James Mckenna, Vassilis Glenis

Pith reviewed 2026-05-10 06:30 UTC · model grok-4.3

classification 💻 cs.NE
keywords urban flood resilienceblue-green infrastructuremulti-objective optimizationevolutionary algorithmhydrodynamic modelingflood risk managementclimate adaptationoptimization tool
0
0 comments X

The pith

A multi-objective optimization tool couples a full hydrodynamic model with a bespoke evolutionary algorithm to design optimal blue-green infrastructure for urban flood resilience.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an optimization framework for placing blue-green infrastructure to reduce flood risks in cities facing more intense storms. It evaluates each potential design using a complete hydrodynamic simulation that predicts flooding at the scale of individual properties, rather than relying on simpler approximations limited to mapping flooded areas. A custom evolutionary algorithm is engineered to require far fewer of these computationally heavy simulations, making the process practical. This setup is intended to deliver planners a trustworthy collection of the best possible solutions for investment decisions.

Core claim

By linking a state-of-the-art hydrodynamic model directly to a tailored evolutionary algorithm, the tool accurately assesses blue-green infrastructure effectiveness at property scale while efficiently searching the design space, delivering greater certainty about solution optimality than methods using simplified inundation models.

What carries the argument

The bespoke evolutionary algorithm that minimizes the number of full hydrodynamic simulations needed to evaluate candidate blue-green infrastructure configurations.

If this is right

  • Compared to traditional design practices, it provides an automated way to examine many more potential solutions.
  • Decision-makers receive a set of optimal solutions to support informed choices on flood management investments.
  • The approach creates a robust framework for optimizing different types of blue-green features in complex urban areas.
  • Validation shows reliable convergence in simple cases and competitive performance against other algorithms in complex ones.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the method works, cities could shift from manual or approximate planning to data-driven placement of flood defenses that better match actual risks.
  • The same coupling of accurate simulation and efficient search might apply to optimizing other urban systems like drainage or green spaces for heat or pollution control.
  • Future testing could involve applying the tool to a specific city district and comparing predicted outcomes with real flood events after implementation.

Load-bearing premise

The bespoke evolutionary algorithm consistently locates near-optimal solutions in large urban design spaces and the hydrodynamic model correctly represents property-level flood behavior without major inaccuracies.

What would settle it

Implement the tool's top recommended blue-green infrastructure layout in a real neighborhood and compare its actual flood performance during storms against both a traditional design and the tool's predictions.

Figures

Figures reproduced from arXiv: 2604.18620 by Christos Iliadis, James Mckenna, Vassilis Glenis.

Figure 1
Figure 1. Figure 1: Example genetic representation of the placement of a range of BGI within a spatial domain. The representation of [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Genotype-phenotype mapping for a zonal feature. The example genotype provides a genetic representation of a candidate solution in which zones 1,3,5,7,8 and 10 of the spatial domain include the specified zonal flood intervention. Zones may be arbitrarily delineated via an automated process or manually delineated to incorporate domain knowledge. A chromosome of length L, representing L zones, results in a ge… view at source ↗
Figure 3
Figure 3. Figure 3: Overall mapping from phenotype to genotype for a continuous component characteristic as described in equation [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison between the three genetic representations used to encode a discretised component characteristic as a [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example genetic representation of a local feature. The representation of the candidate solution for the local feature is [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Genotype-discrete phenotype mapping for a [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A workflow illustrating the logic used by the objective function when evaluating the chosen flood risk metric (number [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: (a) Illustrates a Pareto set, P = {p1, p2, . . . , p7}, whose points are denoted by an annotated red ×. The region dominated by the Pareto front is outlined in red and dominated solutions contained within the feasible objective space, Z, are illustrated in grey. (b) illustrates the dominance relation defined in Definition 2.1, whereby, for a minimisation problem, z dominates the shaded region. 2.1.3. Expos… view at source ↗
Figure 9
Figure 9. Figure 9: Schematic workflow of the flood exposure analysis tool for the classification of buildings according to the water depth [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Flood damages for different water depths in the North East of England for short duration storms without warning [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: (a) Illustrates a ϵ−Pareto set, Pϵ = {p1, p3, p5, p7}, whose points are denoted by an annotated red crosses (×). Note that Pϵ ⊂ P, as the Pareto optimal points {p2, p4, p6} are box-dominated. The box-dominated region is outlined in red and box dominated solutions contained within the feasible objective space, Z, are denoted by grey crosses (×). (b) illustrates the box dominance relation defined in Definit… view at source ↗
Figure 12
Figure 12. Figure 12: The potential relative locations of the nadir objective vector ( [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Illustration of the S-metric (hyper-volume indicator) for a Pareto front approximation, represented by the grey [PITH_FULL_IMAGE:figures/full_fig_p028_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Overview of the study area. modelled as a function of the soils hydraulic conductivity, porosity and suction head using the Green-Ampt method [6]. Impermeable areas are assigned the following properties: hydraulic conductivity = 1.09cm/hr, wetting front suction head = 11.01cm, effective porosity = 0.412, effective saturation = 0.30. For permeable paving, the properties are modified to be: hydraulic conduc… view at source ↗
Figure 15
Figure 15. Figure 15: A plot showing all 4,096 feasible objective vectors for the twelve zonal feature test scenario. Pareto optimal vectors [PITH_FULL_IMAGE:figures/full_fig_p032_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Comparison between the hyper-volume ratio (S-metric (%)) versus the number of fitness evaluations for the three [PITH_FULL_IMAGE:figures/full_fig_p032_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Performance of the algorithms for varying maximum archive sizes ( [PITH_FULL_IMAGE:figures/full_fig_p033_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: The spatial domain for Test Case II. use of a much larger search space enables for a clearer understanding of the performance differences which are mostly indistinguishable for Test Case I. However, by using a large search space, an exhaustive search of the search space is rendered intractable. As a result, the Pareto set is unknown, and inter-algorithm comparisons provide the only meaningful basis for an… view at source ↗
Figure 19
Figure 19. Figure 19: Comparison between the Pareto front approximations for the three MOEAs after [PITH_FULL_IMAGE:figures/full_fig_p037_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: S-metric per unique simulation for the three MOEAs. [PITH_FULL_IMAGE:figures/full_fig_p037_20.png] view at source ↗
read the original abstract

Due to the increasing frequency and severity of storm events, driven by the escalation of anthropogenic climate change and urban expansion, there is a requirement for increasingly efficient flood risk management strategies. While Blue-Green Infrastructure (BGI) offers a sustainable solution for managing flood risk, optimal implementation is challenging. To help overcome this challenge, this study presents a novel multi-objective optimisation tool that couples a state-of-the-art hydrodynamic model with a bespoke evolutionary algorithm. The use of a fully dynamic hydrodynamic model enables the tool to accurately evaluate the effectiveness of proposed BGI features with respect to property scale flood vulnerability and hazard analysis. This contrasts with alternative approaches which utilise simplified models, which can only reliably predict inundation extents, thus the proposed optimisation tool provides greater certainty regarding the optimality of the solutions. As a hydrodynamic simulation is required to evaluate each candidate solution, the bespoke evolutionary algorithm is specifically designed to minimise the number of simulations required, ensuring the tool is computationally practical. The effectiveness of the tool in this regard is validated via the derivation of exact convergence measures, for a tractable search space, and via comparisons with benchmark algorithms, for an intractable search space. Compared with traditional design practices, the proposed tool offers an automated approach capable of efficiently exploring a wide range of solutions, providing decision-makers with a set of optimal solutions from which they can make informed investment decisions. The presented methods provide a robust framework for optimising a variety of BGI features in complex urban environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript presents a multi-objective optimisation tool that couples a fully dynamic hydrodynamic model with a bespoke evolutionary algorithm to optimise Blue-Green Infrastructure (BGI) placement for urban flood resilience. It claims this yields solutions with greater certainty of optimality than approaches relying on simplified inundation models, because the hydrodynamic model enables accurate property-scale flood vulnerability and hazard evaluation. Computational practicality is asserted via an EA designed to minimise simulations, with validation through exact convergence measures on a tractable search space and benchmark comparisons on an intractable one. The tool is positioned as an automated framework to support informed investment decisions in complex urban environments.

Significance. If the central claims hold, the work offers a practical advance in applying evolutionary computation to real-world flood management by integrating accurate dynamic modeling with optimisation. The explicit validation via exact convergence measures on tractable subspaces is a methodological strength that supports reproducibility and rigor in the EA component. This could meaningfully aid decision-makers facing climate-driven flood risks, provided the near-optimality and model fidelity assumptions are substantiated.

major comments (1)
  1. [Abstract (validation paragraph)] The central claim of 'greater certainty regarding the optimality of the solutions' (Abstract) rests on the bespoke EA reliably identifying near-optimal points in high-dimensional urban search spaces when evaluated under the hydrodynamic model. However, the validation for intractable spaces relies solely on benchmark comparisons, which establish only relative performance among algorithms. This does not rule out the possibility that all compared methods remain far from the true optimum, weakening the asserted gain in certainty over simplified-model approaches. A concrete test or additional analysis (e.g., on smaller tractable instances with known optima or theoretical bounds) is needed to support the load-bearing claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We are grateful to the referee for their detailed review and for recognizing the potential of our work in advancing evolutionary computation applications to flood management. We address the major comment point by point below.

read point-by-point responses

  1. Referee: [Abstract (validation paragraph)] The central claim of 'greater certainty regarding the optimality of the solutions' (Abstract) rests on the bespoke EA reliably identifying near-optimal points in high-dimensional urban search spaces when evaluated under the hydrodynamic model. However, the validation for intractable spaces relies solely on benchmark comparisons, which establish only relative performance among algorithms. This does not rule out the possibility that all compared methods remain far from the true optimum, weakening the asserted gain in certainty over simplified-model approaches. A concrete test or additional analysis (e.g., on smaller tractable instances with known optima or theoretical bounds) is needed to support the load-bearing claim.

    Authors: We acknowledge the validity of this observation. Benchmark comparisons indeed only demonstrate relative performance and cannot confirm absolute near-optimality in intractable spaces. The manuscript's claim of greater certainty is grounded in the accurate property-scale flood vulnerability assessment enabled by the full hydrodynamic model, as opposed to simplified models that are limited to inundation extents. The bespoke EA is validated through exact convergence to the known optimum in tractable search spaces, and through superior performance against several benchmark algorithms in the intractable case. To strengthen the support for the claim, we will include additional analysis on smaller tractable instances where exhaustive search provides known optima, and add a section discussing the relative nature of benchmark validations and the assumptions involved. We will also revise the abstract to more precisely articulate the source of the increased certainty. These changes will be incorporated in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: validation relies on external benchmarks and exact measures, not self-definition or fitted inputs

full rationale

The paper's core claim is that coupling a full hydrodynamic model with a bespoke EA yields greater certainty on BGI optimality than simplified inundation models. Validation proceeds via derivation of exact convergence measures on a tractable subspace and relative benchmark comparisons on an intractable one; neither step reduces by construction to the inputs, nor renames a fit as a prediction, nor imports uniqueness via self-citation. The derivation chain remains self-contained against the stated external benchmarks and does not exhibit any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract; no explicit free parameters, axioms, or invented entities are detailed. The approach implicitly assumes the hydrodynamic model is sufficiently accurate and that the evolutionary algorithm can navigate the search space effectively.

pith-pipeline@v0.9.0 · 5557 in / 1040 out tokens · 49320 ms · 2026-05-10T06:30:19.521187+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

140 extracted references · 94 canonical work pages

  1. [1]

    Vasiliades, Hydrological hazards: The 4Ms — Modelling, Monitoring, Management, and Mit- igation, in: P

    L. Vasiliades, Hydrological hazards: The 4Ms — Modelling, Monitoring, Management, and Mit- igation, in: P. Koundouri, A. Alamanos (Eds.), Elgar Encyclopedia of Water Policy, Eco- nomics and Management, Edward Elgar Publishing, Cheltenham, UK, 2024, Ch. 26, pp. 112–117. doi:10.4337/9781802202946.00033

    work page doi:10.4337/9781802202946.00033 2024

  2. [2]

    Mignot, B

    E. Mignot, B. Dewals, Hydraulic modelling of inland urban flooding: Recent advances, Journal of Hydrology 609 (2022). doi:10.1016/j.jhydrol.2022.127763

    work page doi:10.1016/j.jhydrol.2022.127763 2022

  3. [3]

    O’Donnell, C

    E. O’Donnell, C. Thorne, S. Ahilan, S. Arthur, S. Birkinshaw, D. Butler, D. Dawson, G. Everett, R. Fenner, V. Glenis, L. Kapetas, C. Kilsby, V. Krivtsov, J. Lamond, S. Maskrey, G. O’Donnell, K. Potter, K. Vercruysse, T. Vilcan, N. Wright, The blue-green path to urban flood resilience, Blue- Green Systems 2 (1) (2020) 28–45. doi:10.2166/bgs.2019.199

    work page doi:10.2166/bgs.2019.199 2020

  4. [4]

    Galiatsatou, C

    P. Galiatsatou, C. Iliadis, Intensity-duration-frequency curves at ungauged sites in a changing climate for sustainable stormwater networks, Sustainability 14 (3) (2022). doi:10.3390/su14031229

    work page doi:10.3390/su14031229 2022

  5. [5]

    B. R. Rosenzweig, P. Herreros Cantis, Y. Kim, A. Cohn, K. Grove, J. Brock, J. Yesuf, P. Mistry, C. Welty, T. McPhearson, J. Sauer, H. Chang, The value of urban flood modeling, Earth’s Future 9 (1) (2021) e2020EF001739. doi:10.1029/2020EF001739

    work page doi:10.1029/2020ef001739 2021

  6. [6]

    Glenis, V

    V. Glenis, V. Kutija, C. Kilsby, A fully hydrodynamic urban flood modelling system representing buildings, green space and interventions, Environmental Modelling & Software 109 (2018) 272–292. doi:10.1016/j.envsoft.2018.07.018

    work page doi:10.1016/j.envsoft.2018.07.018 2018

  7. [7]

    K. Guo, M. Guan, D. Yu, Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci. 25 (5) (2021) 2843–2860, hESS. doi:10.5194/hess-25-2843-2021

    work page doi:10.5194/hess-25-2843-2021 2021

  8. [8]

    B. F. Sanders, Hydrodynamic modeling of urban flood flows and disaster risk reduction (2017-03-29 2017). doi:10.1093/acrefore/9780199389407.013.127

    work page doi:10.1093/acrefore/9780199389407.013.127 2017

  9. [9]

    J. Teng, A. J. Jakeman, J. Vaze, B. F. Croke, D. Dutta, S. Kim, Flood inundation modelling: A review of methods, recent advances and uncertainty analysis, Environmental Modelling and Software 90 (2017) 201–216. doi:10.1016/j.envsoft.2017.01.006

    work page doi:10.1016/j.envsoft.2017.01.006 2017

  10. [10]

    URLhttps://downloads.tuflow.com/_archive/TUFLOW/Releases/2018-03/TUFLOW%20Manual.2018-03.pdf

    TUFLOW, 1D/2D Fixed Grid Hydraulic Modelling: TUFLOW Classic/HPC User Manual, build 2018-03-ad Edition, TUFLOW, 2018. URLhttps://downloads.tuflow.com/_archive/TUFLOW/Releases/2018-03/TUFLOW%20Manual.2018-03.pdf

    2018

  11. [11]

    Alves, A

    A. Alves, A. Sanchez, B. Gersonius, Z. Vojinovic, A model-based framework for selection and devel- opment of multi-functional and adaptive strategies to cope with urban floods, Procedia Engineering 154 (2016) 877–884. doi:10.1016/j.proeng.2016.07.463

    work page doi:10.1016/j.proeng.2016.07.463 2016

  12. [12]

    Morgan, R

    M. Morgan, R. Fenner, Spatial evaluation of the multiple benefits of sustainable drainage sys- tems, Proceedings of the Institution of Civil Engineers - Water Management 172 (1) (2019) 39–52. doi:10.1680/jwama.16.00048

    work page doi:10.1680/jwama.16.00048 2019

  13. [13]

    Singh, D

    A. Singh, D. Dawson, M. Trigg, N. Wright, A review of modelling methodologies for flood source area (FSA) identification, Natural Hazards 107 (2) (2021) 1047–1068. doi:10.1007/s11069-021-04672-2

    work page doi:10.1007/s11069-021-04672-2 2021

  14. [14]

    Ferrans, J

    P. Ferrans, J. D. Reyes-Silva, P. Krebs, J. Temprano, Flood management with SUDS: A simula- tion–optimization framework (2023). doi:10.3390/w15030426. 42

    work page doi:10.3390/w15030426 2023

  15. [15]

    X. Xie, Q. Chu, Z. Qiu, G. Liu, S. Jia, Identifying the optimal layout of low-impact development measures at an urban watershed scale using a multi-objective decision-making framework (2024). doi:10.3390/w16141969

    work page doi:10.3390/w16141969 2024

  16. [16]

    Djordjević, D

    S. Djordjević, D. Prodanović, C. Maksimović, An approach to simulation of dual drainage, Water Science and Technology 39 (9) (1999) 95–103. doi:10.1016/S0273-1223(99)00221-8

    work page doi:10.1016/s0273-1223(99)00221-8 1999

  17. [17]

    Costabile, et al., Flood mapping using LIDAR DEM

    P. Costabile, et al., Flood mapping using LIDAR DEM. limitations of the 1-D modeling highlighted by the 2-D approach, Natural Hazards 77 (1) (2015) 181–204. doi:10.1007/s11069-015-1606-0

    work page doi:10.1007/s11069-015-1606-0 2015

  18. [18]

    Costabile, F

    P. Costabile, F. Macchione, Analysis of one-dimensional modelling for flood routing in compound channels, Water Resources Management 26 (5) (2012) 1065–1087. doi:10.1007/s11269-011-9947-2

    work page doi:10.1007/s11269-011-9947-2 2012

  19. [19]

    Petaccia, et al., Flood wave propagation in steep mountain rivers, Journal of Hydroinformatics 15 (1) (2012) 120–137

    G. Petaccia, et al., Flood wave propagation in steep mountain rivers, Journal of Hydroinformatics 15 (1) (2012) 120–137. doi:10.2166/hydro.2012.122

    work page doi:10.2166/hydro.2012.122 2012

  20. [20]

    P. D. Bates, M. S. Horritt, T. J. Fewtrell, A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, Journal of Hydrology 387 (1) (2010) 33–45. doi:10.1016/j.jhydrol.2010.03.027

    work page doi:10.1016/j.jhydrol.2010.03.027 2010

  21. [21]

    Costabile, C

    P. Costabile, C. Costanzo, F. Macchione, Performances and limitations of the diffusive approximation of the 2-d shallow water equations for flood simulation in urban and rural areas, Applied Numerical Mathematics 116 (2017) 141–156. doi:10.1016/j.apnum.2016.07.003

    work page doi:10.1016/j.apnum.2016.07.003 2017

  22. [22]

    J. E. Miller, Basic concepts of kinematic-wave models, Report 1302, U.S. Geological Survey (1984). doi:10.3133/pp1302. URLhttps://pubs.usgs.gov/publication/pp1302

    work page doi:10.3133/pp1302 1984

  23. [23]

    Costabile, C

    P. Costabile, C. Costanzo, G. De Lorenzo, F. Macchione, Is local flood hazard assessment in urban areas significantly influenced by the physical complexity of the hydrodynamic inundation model?, Journal of Hydrology 580 (2020) 124231. doi:10.1016/j.jhydrol.2019.124231

    work page doi:10.1016/j.jhydrol.2019.124231 2020

  24. [24]

    B. e. a. Dewals, Can the 2D shallow water equations model flow intrusion into buildings during urban floods?, Journal of Hydrology 619 (2023) 129231. doi:10.1016/j.jhydrol.2023.129231

    work page doi:10.1016/j.jhydrol.2023.129231 2023

  25. [25]

    L. e. a. Liu, Building performance in dam-break flow - an experimental study, Urban Water Journal 15 (3) (2018) 251–258. doi:10.1080/1573062X.2018.1433862

    work page doi:10.1080/1573062x.2018.1433862 2018

  26. [26]

    Mignot, L

    E. Mignot, L. Camusson, N. Riviere, Measuring the flow intrusion towards building areas during urban floods: Impact of the obstacles located in the streets and on the facade, Journal of Hydrology 583 (2020) 124607. doi:10.1016/j.jhydrol.2020.124607

    work page doi:10.1016/j.jhydrol.2020.124607 2020

  27. [27]

    Bazin, E

    P.-H. Bazin, E. Mignot, A. Paquier, Computing flooding of crossroads with obstacles using a 2d numer- ical model, Journal of Hydraulic Research 55 (1) (2017) 72–84. doi:10.1080/00221686.2016.1217947

    work page doi:10.1080/00221686.2016.1217947 2017

  28. [28]

    Paquié, E

    A. Paquié, E. Mignot, P.-H. Bazin, From hydraulic modelling to urban flood risk, Procedia Engineering 115 (2015) 37–44. doi:10.1016/j.proeng.2015.07.352

    work page doi:10.1016/j.proeng.2015.07.352 2015

  29. [29]

    P. H. e. a. Bazin, Influence of detailed topography when modeling flows in street junction during urban flood, Journal of Disaster Research 7 (5) (2012) 560–566. URLhttps://hal.inrae.fr/hal-02597823

    2012

  30. [30]

    Cozzolino, L

    L. Cozzolino, L. Cimorelli, R. Della Morte, G. Pugliano, V. Piscopo, D. Pianese, Flood propagation modeling with the local inertia approximation: Theoretical and numerical analysis of its physical limitations, Advances in Water Resources 133 (2019) 103422. doi:10.1016/j.advwatres.2019.103422. 43

    work page doi:10.1016/j.advwatres.2019.103422 2019

  31. [31]

    URLhttp://data.europa.eu/eli/dir/2000/60/oj

    The European Parliament and the Council of the European Union, Directive 2000/60/ec of the euro- pean parliament and of the council (Oct 2000). URLhttp://data.europa.eu/eli/dir/2000/60/oj

    2000

  32. [32]

    Woods Ballard, S

    B. Woods Ballard, S. Wilson, H. Udale-Clarke, S. Illman, T. Scott, R. Ashley, R. Kellagher, The SuDS Manual (C753F), CIRIA (Construction Industry Research and Information Association), 2015. URLhttps://www.ciria.org/ItemDetail?iProductCode=C753F&Category=FREEPUBS

    2015

  33. [33]

    J. Luo, P. Hu, J. Sun, N. Yang, Q. Liu, N. Qin, Y. Yuan, G. Xu, Research on multiobjective optimiza- tion of sponge city based on SWMM model, Mobile Information Systems 2022 (1) (2022) 2677518. doi:10.1155/2022/2677518

    work page doi:10.1155/2022/2677518 2022

  34. [34]

    K. Deb, S. Agrawal, A. Pratab, T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in: M. e. a. Schoenauer (Ed.), Proceedings of the Parallel Problem Solving from Nature VI Conference, Springer, 2000, pp. 849–858

    2000

  35. [35]

    Metcalf, Storm water management model, volume i-final report, EPA Report 11024 DOC 07/71 (NTIS PB-203289), Environmental Protection Agency, Washington, DC (1971)

    E. Metcalf, Storm water management model, volume i-final report, EPA Report 11024 DOC 07/71 (NTIS PB-203289), Environmental Protection Agency, Washington, DC (1971)

    1971

  36. [36]

    S. Wan, L. Xu, Q. Qi, H. Yang, Y. Zhou, Building a multi-objective optimization model for sponge city projects, Urban Climate 43 (2022) 101171. doi:10.1016/j.uclim.2022.101171

    work page doi:10.1016/j.uclim.2022.101171 2022

  37. [37]

    X. Li, X. Zhou, J. Hou, Y. Liu, S. Xue, H. Ma, B. Su, A hydrodynamic model and data-driven evolutionary multi-objective optimization algorithm based optimal operation method for multi-barrage flood control, Water Resources Management 38 (11) (2024) 4323–4341. doi:10.1007/s11269-024-03867- z

    work page doi:10.1007/s11269-024-03867- 2024

  38. [38]

    Y. Zhu, C. Xu, Z. Liu, D. Yin, H. Jia, Y. Guan, Spatial layout optimization of green infrastructure based on life-cycle multi-objective optimization algorithm and swmm model, Resources, Conservation and Recycling 191 (2023) 106906. doi:10.1016/j.resconrec.2023.106906

    work page doi:10.1016/j.resconrec.2023.106906 2023

  39. [39]

    B. Yang, T. Zhang, J. Li, P. Feng, Y. Miao, Optimal designs of LID based on LID experiments and SWMM for a small-scale community in tianjin, north china, Journal of Environmental Management 334 (2023) 117442. doi:10.1016/j.jenvman.2023.117442

    work page doi:10.1016/j.jenvman.2023.117442 2023

  40. [40]

    H. Zhou, C. Gao, Q. Luan, L. Shi, Z. Lu, J. Liu, Multi-objective optimization of distributed green in- frastructure for effective stormwater management in space-constrained highly urbanized areas, Journal of Hydrology 644 (2024) 132065. doi:10.1016/j.jhydrol.2024.132065

    work page doi:10.1016/j.jhydrol.2024.132065 2024

  41. [41]

    Duarte Lopes, G

    M. Duarte Lopes, G. Barbosa Lima da Silva, An efficient simulation-optimization approach based on genetic algorithms and hydrologic modeling to assist in identifying optimal low impact development designs, Landscape and Urban Planning 216 (2021) 104251. doi:10.1016/j.landurbplan.2021.104251

    work page doi:10.1016/j.landurbplan.2021.104251 2021

  42. [42]

    M. R. Hassani, M. H. Niksokhan, S. F. Mousavi Janbehsarayi, M. R. Nikoo, Multi-objective robust decision-making for LIDs implementation under climatic change, Journal of Hydrology 617 (2023) 128954. doi:10.1016/j.jhydrol.2022.128954

    work page doi:10.1016/j.jhydrol.2022.128954 2023

  43. [43]

    M. R. Hassani, S. F. Mousavi Janbehsarayi, M. H. Niksokhan, A. Sharma, Intersecting social welfare with resilience to streamline urban flood management, Sustainable Cities and Society 116 (2024) 105927. doi:10.1016/j.scs.2024.105927

    work page doi:10.1016/j.scs.2024.105927 2024

  44. [44]

    S. F. Mousavi Janbehsarayi, M. H. Niksokhan, M. R. Hassani, M. Ardestani, Multi-objective decision- making based on theories of cooperative game and social choice to incentivize implementation of low-impact development practices, Journal of Environmental Management 330 (2023) 117243. doi:10.1016/j.jenvman.2023.117243. 44

    work page doi:10.1016/j.jenvman.2023.117243 2023

  45. [45]

    Tansar, H.-F

    H. Tansar, H.-F. Duan, O. Mark, A multi-objective decision-making framework for implementing green-grey infrastructures to enhance urban drainage system resilience, Journal of Hydrology 620 (2023) 129381. doi:10.1016/j.jhydrol.2023.129381

    work page doi:10.1016/j.jhydrol.2023.129381 2023

  46. [46]

    S. Li, Z. Wang, X. Wu, Z. Zeng, P. Shen, C. Lai, A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC, Journal of Environmental Management 307 (2022) 114574. doi:10.1016/j.jenvman.2022.114574

    work page doi:10.1016/j.jenvman.2022.114574 2022

  47. [47]

    Y. Yu, Y. Zhou, Z. Guo, B. van Duin, W. Zhang, A new LID spatial allocation optimization system at neighborhood scale: Integrated SWMM with PICEA-g using MATLAB as the platform, Science of The Total Environment 831 (2022) 154843. doi:10.1016/j.scitotenv.2022.154843

    work page doi:10.1016/j.scitotenv.2022.154843 2022

  48. [48]

    J. Wu, J. Xu, M. Lu, H. Ming, An integrated modelling framework for optimization of the place- ment of grey-green-blue infrastructure to mitigate and adapt flood risk: An application to the Upper Ting River Watershed, China, Journal of Hydrology: Regional Studies 57 (2025) 102156. doi:10.1016/j.ejrh.2024.102156

    work page doi:10.1016/j.ejrh.2024.102156 2025

  49. [49]

    Taghizadeh, S

    S. Taghizadeh, S. Khani, T. Rajaee, Hybrid SWMM and particle swarm optimization model for urban runoff water quality control by using green infrastructures (LID-BMPs), Urban Forestry & Urban Greening 60 (2021) 127032. doi:10.1016/j.ufug.2021.127032

    work page doi:10.1016/j.ufug.2021.127032 2021

  50. [50]

    Macro, L

    K. Macro, L. S. Matott, A. Rabideau, S. H. Ghodsi, Z. Zhu, OSTRICH-SWMM: A new multi-objective optimization tool for green infrastructure planning with SWMM, Environmental Modelling & Software 113 (2019) 42–47. doi:10.1016/j.envsoft.2018.12.004

    work page doi:10.1016/j.envsoft.2018.12.004 2019

  51. [51]

    Shojaeizadeh, M

    A. Shojaeizadeh, M. Geza, T. S. Hogue, GIP-SWMM: A new green infrastructure place- ment tool coupled with SWMM, Journal of Environmental Management 277 (2021) 111409. doi:10.1016/j.jenvman.2020.111409

    work page doi:10.1016/j.jenvman.2020.111409 2021

  52. [52]

    Talebi, M

    A. Talebi, M. Dolatshahi, R. Kerachian, A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting, Journal of Environmental Management 350 (2024) 119638. doi:10.1016/j.jenvman.2023.119638

    work page doi:10.1016/j.jenvman.2023.119638 2024

  53. [53]

    W. Chen, W. Wang, C. Mei, Y. Chen, P. Zhang, P. Cong, Multi-objective decision-making for green infrastructure planning: Impacts of rainfall characteristics and infrastructure configuration, Journal of Hydrology 628 (2024) 130572. doi:10.1016/j.jhydrol.2023.130572

    work page doi:10.1016/j.jhydrol.2023.130572 2024

  54. [54]

    Eckart, Z

    K. Eckart, Z. McPhee, T. Bolisetti, Multiobjective optimization of low impact development stormwater controls, Journal of Hydrology 562 (2018) 564–576. doi:10.1016/j.jhydrol.2018.04.068

    work page doi:10.1016/j.jhydrol.2018.04.068 2018

  55. [55]

    Singh, D

    V. Singh, D. Frevert (Eds.), Watershed Models, 1st Edition, CRC Press, 2010. doi:10.1201/9781420037432

    work page doi:10.1201/9781420037432 2010

  56. [56]

    L. A. Rossman, Storm Water Management Model Reference Manual, Volume II – Hydraulics, U.S. Environmental Protection Agency, Cincinnati, OH (May 2017)

    2017

  57. [57]

    Ur Rehman, V

    A. Ur Rehman, V. Glenis, E. Lewis, C. Kilsby, Multi-objective optimisation framework for Blue- Green infrastructure placement using detailed flood model, Journal of Hydrology 638 (2024) 131571. doi:10.1016/j.jhydrol.2024.131571

    work page doi:10.1016/j.jhydrol.2024.131571 2024

  58. [58]

    A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, 2nd Edition, Natural Computing Series, Springer, Berlin, Heidelberg, 2015. doi:10.1007/978-3-662-44874-8

    work page doi:10.1007/978-3-662-44874-8 2015

  59. [59]

    D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989. 45

    1989

  60. [60]

    J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975

    1975

  61. [61]

    D. E. Goldberg, Real-coded genetic algorithms, virtual alphabets, and blocking, Complex Systems 5 (2) (1991) 139–167

    1991

  62. [62]

    Rothlauf, D

    F. Rothlauf, D. E. Goldberg, Redundant representations in evolutionary computation, Evolutionary Computation 11 (4) (2003) 381–415. doi:10.1162/106365603322519288

    work page doi:10.1162/106365603322519288 2003

  63. [63]

    doi:10.1007/3-540-32444-5

    F.Rothlauf, RepresentationsforGeneticandEvolutionaryAlgorithms, 2ndEdition, Vol.104ofStudies in Fuzziness and Soft Computing, Springer-Verlag Berlin Heidelberg, 2006. doi:10.1007/3-540-32444-5

    work page doi:10.1007/3-540-32444-5 2006

  64. [64]

    Laumanns, L

    M. Laumanns, L. Thiele, E. Zitzler, Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions, IEEE Transactions on Evolutionary Computation 8 (2) (2004) 170–182. doi:10.1109/TEVC.2004.823470

    work page doi:10.1109/tevc.2004.823470 2004

  65. [65]

    O. Giel, P. K. Lehre, On the effect of populations in evolutionary multi-objective optimisation, Evo- lutionary Computation 18 (3) (2010) 335–356. doi:10.1162/EVCO_a_00013

    work page doi:10.1162/evco_a_00013 2010

  66. [66]

    Covantes Osuna, W

    E. Covantes Osuna, W. Gao, F. Neumann, D. Sudholt, Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation, Theoretical Computer Science 832 (2020) 123–142, theory of Evolutionary Computation. doi:10.1016/j.tcs.2018.06.009

    work page doi:10.1016/j.tcs.2018.06.009 2020

  67. [67]

    Doerr, Z

    B. Doerr, Z. Qu, A first runtime analysis of the NSGA-II on a multimodal problem, in: G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII, Springer International Publishing, Cham, 2022, pp. 399–412

    2022

  68. [68]

    Zheng, Y

    W. Zheng, Y. Liu, B. Doerr, A first mathematical runtime analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), in: Proceedings of the 36th Conference on Artificial Intelligence, Vol. 36, 2022, pp. 10408–10416. doi:10.1609/aaai.v36i9.21283

    work page doi:10.1609/aaai.v36i9.21283 2022

  69. [69]

    Doerr, Z

    B. Doerr, Z. Qu, From understanding the population dynamics of the NSGA-II to the first proven lower bounds, in: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty- Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, AAAI’23...

    work page doi:10.1609/aaai.v37i10.26462 2023

  70. [70]

    M. Lim, A. Teoh, K. Toh, An analysis on equal width quantization and linearly separable subcode encoding-based discretization and its performance resemblances, EURASIP Journal on Advances in Signal Processing 2011 (2011) 82. doi:10.1186/1687-6180-2011-82

    work page doi:10.1186/1687-6180-2011-82 2011

  71. [71]

    Hinterding, H

    R. Hinterding, H. Gielewski, T. C. Peachey, The nature of mutation in genetic algorithms, in: Pro- ceedings of the 6th International Conference on Genetic Algorithms, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1995, p. 65–72

    1995

  72. [72]

    D. A. V. Veldhuizen, G. B. Lamont, Multiobjective evolutionary algorithm test suites, in: Proceedings of the 1999 ACM Symposium on Applied Computing, 1999, pp. 351–357

    1999

  73. [73]

    Gray, Pulse code communications (1953)

    F. Gray, Pulse code communications (1953)

    1953

  74. [74]

    U. K. Chakraborty, C. Z. Janikow, An analysis of gray versus binary encoding in genetic search, Information Sciences: An International Journal; Special Issue: Evolutionary Computation 156 (2003) 253–269

    2003

  75. [75]

    K. E. Mathias, D. Whitley, Transforming the search space with Gray coding, in: IEEE International Conference on Evolutionary Computation, IEEE Service Center, 1994, pp. 513–518. 46

    1994

  76. [77]

    Whitley, A Free Lunch proof for Gray versus binary encodings, in: GECCO-99, Morgan Kaufmann, 1999, pp

    D. Whitley, A Free Lunch proof for Gray versus binary encodings, in: GECCO-99, Morgan Kaufmann, 1999, pp. 726–733

    1999

  77. [78]

    S. C. Chiam, C. K. Goh, K. C. Tan, Issues of binary representation in evolutionary algorithms, in: 2006 IEEE Conference on Cybernetics and Intelligent Systems, IEEE, 2006, pp. 1–8

    2006

  78. [79]

    Shackleton, R

    M. Shackleton, R. Shipman, M. Ebner, An investigation of redundant genotype-phenotype mappings and their role in evolutionary search, in: Proceedings of the 2000 Congress on Evolutionary Compu- tation CEC00, IEEE Press, La Jolla Marriott Hotel, La Jolla, California, USA, 2000, pp. 493–500

    2000

  79. [80]

    Wolpert, W

    D. Wolpert, W. Macready, No Free Lunch theorems for optimisation, IEEE Transactions on Evolu- tionary Computation 1 (1) (1997) 67–82

    1997

  80. [81]

    Shastri, E

    H. Shastri, E. Frachtenberg, Revisiting locality in binary-integer representations (2020). arXiv:2007.12159, doi:10.48550/arXiv.2007.12159

    work page doi:10.48550/arxiv.2007.12159 2020

Showing first 80 references.