Abstract
Nature-Based Solutions can be considered one of the best answers to the various consequences and problems caused by climate change, poor urbanisation and population growth. They are used not only as measures for the protection, sustainable management and restoration of natural and modified ecosystems but also as measures to mitigate certain natural disasters such as erosion, flooding, drought, storm surge and landslide. The benefit is for both biodiversity and human well-being. This paper reviews articles about optimising the selection and placement of Nature-Based Solutions. It presents several Operations Research approaches used in the context of climate adaptation. The analysis provided in this paper focuses on various case studies, state-of-the-art on Nature-Based Solutions, Operations Research algorithms, dissertations, and other papers dealing with infrastructure placement approaches in the context of climate adaptation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- AHP:
-
Analytic hierarchy process
- AMALGAM:
-
Approach a multiALgorithm, Genetically Adaptive Multi-objective
- AMS:
-
Adaptive metropolis search
- B&B:
-
Branch & bound
- BMP:
-
Best management practices
- FIFO:
-
First in first out
- GIS:
-
Geographic information system
- GSA:
-
Gravitational search algorithm
- IP:
-
Integer programming
- LHS:
-
Latin hypercube sampling
- LID:
-
Low-impact development
- LP:
-
Linear programming
- L-THIA-LID:
-
Long-term hydrologic impact assessment-low-impact development
- MILP:
-
Mixed-integer linear programming
- MOP:
-
Multi-objective problem
- MOPSO:
-
Multi-objective particle-swarm optimisation
- MOSA:
-
Multi-objective simulated annealing
- MOSS:
-
Multi-objective scatter search
- MOTS:
-
Multi-objective tabu search
- MUSIC:
-
Model for urban stormwater improvement conceptualisation
- NBS:
-
Nature-based solutions
- NOx:
-
Nitrate and nitrite
- NP:
-
Non polynomial
- NSGA-II:
-
Non-dominated sorting genetic algorithm II
- PSO:
-
Particle-swarm optimisation
- SA:
-
Simulated annealing
- SBPAT:
-
Structural BMP prioritisation and analysis tool
- SOP:
-
Single-objective problem
- SUSTAIN:
-
System for urban stormwater treatment and analysis integration
- SWAT:
-
Soil and water assessment
- SWMM:
-
Storm water management model
- TKN:
-
Total Kjeldahl nitrogen
- TP:
-
Total phosphorous
- TS:
-
Tabu search
- TSS:
-
Total suspended solids
- USDA:
-
United States Department of Agriculture
- US EPA:
-
United States Environmental Protection Agency
- WMOST:
-
Watershed management optimisation support tool
References
Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. Computational intelligence for multimedia big data on the cloud with engineering applications. Elsevier, Amsterdam, pp 185–231
Al-Harbi KMAS (2001) Application of the AHP in project management. Int J Project Manage 19(1):19–27. https://doi.org/10.1016/S0263-7863(99)00038-1
Alves A, Gersonius B, Kapelan Z et al (2019) Assessing the co-benefits of green-blue-grey infrastructure for sustainable urban flood risk management. J Environ Manage 239:244–254. https://doi.org/10.1016/j.jenvman.2019.03.036
Alves A, Vojinovic Z, Kapelan Z et al (2020) Exploring trade-offs among the multiple benefits of green-blue-grey infrastructure for urban flood mitigation. Sci Total Environ 703(134980):134980. https://doi.org/10.1016/j.scitotenv.2019.134980
Amine K (2019) Multiobjective simulated annealing: principles and algorithm variants. Adv Oper Res 2019:1–13. https://doi.org/10.1155/2019/8134674
Beausoleil RP (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. Eur J Oper Res 169(2):426–449
Behroozi A, Niksokhan MH, Nazariha M (2018) Developing a simulation-optimisation model for quantitative and qualitative control of urban run-off using best management practices. J Flood Risk Manag 11:S340–S351. https://doi.org/10.1111/jfr3.12210
Bellman R (1954) The theory of dynamic programming. Bull New Ser Am Math Soc 60(6):503–515
Boyd S, Mattingley J (2010) Branch and bound methods. Tech. rep., Notes for EE364b, Stanford University
Chang CL, Chiueh PT, Lo SL (2007) Effect of spatial variability of storm on the optimal placement of best management practices (BMPs). Environ Monit Assess 135(1–3):383–389. https://doi.org/10.1007/s10661-007-9657-7
Chung M (2010) Bmp decision using genetic algorithms for cost-effective pollution control at the watershed-level. University of California, Los Angeles, Tech. rep
Cococcioni M, Pappalardo M, Sergeyev YD (2018) Lexicographic multi-objective linear programming using grossone methodology: theory and algorithm. Appl Math Comput 318:298–311. https://doi.org/10.1016/j.amc.2017.05.058
Coelho D, Madureira A, Pereira I et al (2022) Multi-objective evolutionary algorithms and metaheuristics for feature selection: a review. Int J Comput Inf Syst Ind Manag Appl 14:285–296
Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279. https://doi.org/10.1109/TEVC.2004.826067
Cohen-Shacham E, Walters G, Janzen C et al (2016) Nature-based solutions to address global societal challenges. IUCN 97:2016–2036
Damodaram C, Zechman EM (2013) Simulation-optimization approach to design low impact development for managing peak flow alterations in urbanizing watersheds. J Water Resour Plan Manag 139(3):290–298. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000251
Davies C, MacFarlane R, McGloin C et al (2006) Green infrastructure planning guide. Final Report, Project, pp 145–151
Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197. https://doi.org/10.1109/4235.996017
Detenbeck N, Tenbrink M, Abele R, et al (2016) Watershed management optimization support tool (wmost) v2: user manual and case studies. Tech. rep., U.S. EPA Office of Research and Development
Dikin I (1967) Iterative solution of problems of linear and quadratic programming. In: Doklady Akademii Nauk, Russian Academy of Sciences, pp 747–748
EPA (2021) Stormwater best management practice,dry detention ponds. EPA-832-f-21-031a. Tech. rep., United States Environmental Protection Agency (EPA)
Fan CY, Field R, Lai FH, et al (2000) Costs of urban stormwater control. In: Building partnerships. American Society of Civil Engineers, Reston, VA, 10.1061/40517(2000)38
Fausto F, Reyna-Orta A, Cuevas E et al (2020) From ants to whales: metaheuristics for all tastes. Artif Intell Rev 53(1):753–810. https://doi.org/10.1007/s10462-018-09676-2
Fishburn PC (1967) Letter to the editor-additive utilities with incomplete product sets: application to priorities and assignments. Oper Res 15(3):537–542. https://doi.org/10.1287/opre.15.3.537
Galindo Calderon RE (2015) Multi-objective optimization of BMP system configuration for urban runoff reduction. 10.25831/z90a-d940
Giacomoni MH, Joseph J (2017) Multi-objective evolutionary optimization and Monte Carlo simulation for placement of low impact development in the catchment scale. J Water Resour Plan Manag 143(9):04017053. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000812
Gitau MW, Veith TL, Gburek WJ (2004) Farm-level optimization of bmp placement for cost-effective pollution reduction. Trans ASAE 47(6):1923–1931. https://doi.org/10.13031/2013.17805
Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8(1):156–166. https://doi.org/10.1111/j.1540-5915.1977.tb01074.x
GreenBlueUrban (consulted in 2023) Blue infrastructure solutions. https://greenblue.com/gb/blue/
Haario H, Saksman E, Tamminen J (2001) An adaptive metropolis algorithm. Bernoulli 7(2):223–242
Haimes Lasdon, Wismer, (1971) On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Trans Syst Man Cybern 1(3):296–297. https://doi.org/10.1109/TSMC.1971.4308298
Hansen MP (1997) Tabu search for multiobjective optimization: MOTS
Hartmanis J (1982) Computers and intractability: a guide to the theory of np-completeness (Michael R. Garey and David S. Johnson). SIAM Rev 24(1):90–91. https://doi.org/10.1137/1024022
Hassanat A, Almohammadi K, Alkafaween E et al (2019) Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information (Basel) 10(12):390. https://doi.org/10.3390/info10120390
Heatwole CD, Bottcher AB, Baldwin LB (1987) Modeling cost-effectiveness of agricultural nonpoint pollution abatement programs on two Florida basins. J Am Water Resour Assoc 23(1):127–131. https://doi.org/10.1111/j.1752-1688.1987.tb00791.x
Hodges CC (2016) Optimization of bmp selection for distributed stormwater treatment networks. Virginia Polytechnic Institute and State University, Tech. rep
Hojjati A, Monadi M, Faridhosseini A et al (2018) Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems. J Hydrol Hydromech 66(3):323–329. https://doi.org/10.2478/johh-2018-0006
Holland JH (1975) Adaptation in natural and artificial systems. MIT Press, Cambridge
Horton B, Digman C, Ashley R et al (2019) B£ st guidance-guidance to assess the benefits of blue and green infrastructure using b£ st. CIRIA, Griffi n Court, p 15
Huang CL, Hsu NS, Liu HJ et al (2018) Optimization of low impact development layout designs for megacity flood mitigation. J Hydrol (Amst) 564:542–558. https://doi.org/10.1016/j.jhydrol.2018.07.044
Jaeggi DM, Parks GT, Kipouros T et al (2008) The development of a multi-objective tabu search algorithm for continuous optimisation problems. Eur J Oper Res 185(3):1192–1212. https://doi.org/10.1016/j.ejor.2006.06.048
Jang S, Cho M, Yoon J et al (2007) Using SWMM as a tool for hydrologic impact assessment. Desalination 212(1–3):344–356. https://doi.org/10.1016/j.desal.2007.05.005
Jia H, Wang Z, Zhen X et al (2017) China’s sponge city construction: a discussion on technical approaches. Front Environ Sci Eng. https://doi.org/10.1007/s11783-017-0984-9
Johnson R, Sample D (2017) A semi-distributed model for locating stormwater best management practices in coastal environments. Environ Model Softw 91:70–86
Kalra M, Tyagi S, Kumar V et al (2021) A comprehensive review on scatter search: techniques, applications, and challenges. Math Probl Eng 2021:1–21. https://doi.org/10.1155/2021/5588486
Karamouz M, Nazif S (2013) Reliability-based flood management in urban watersheds considering climate change impacts. J Water Resour Plan Manag 139(5):520–533. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000345
Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80(5):8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Kennedy J, Eberhart R (2002) Particle swarm optimization. In: Proceedings of ICNN’95 - international conference on neural networks. IEEE, 10.1109/ICNN.1995.488968
Kirkpatrick S, Gelatt C, Vecchi M (1982) Simulated annealing, sa
Klincewicz JG (1992) Avoiding local optima in thep-hub location problem using tabu search and GRASP. Ann Oper Res 40(1):283–302
Kloss C (2008) Managing wet weather with green infrastructure, EPA-833-f-08-010. Tech. rep, United States Environmental Protection Agency (EPA)
Kundzewicz ZW, Takeuchi K (1999) Flood protection and management: quo vadimus? Hydrol Sci J 44(3):417–432. https://doi.org/10.1121/1.4773260
Land AH, Doig AG (1960) An automatic method of solving discrete programming problems. Econometrica 28(3):497. https://doi.org/10.2307/1910129
Lee JG, Selvakumar A, Alvi K et al (2012) A watershed-scale design optimization model for stormwater best management practices. Environ Model Softw 37:6–18. https://doi.org/10.1016/j.envsoft.2012.04.011
Leng L, Jia H, Chen AS et al (2021) Multi-objective optimization for green-grey infrastructures in response to external uncertainties. Sci Total Environ 775(145831):145831. https://doi.org/10.1016/j.scitotenv.2021.145831
Li N, Qin C, Du P (2018) Optimization of China sponge city design: the case of Lincang technology innovation park. Water (Basel) 10(9):1189. https://doi.org/10.3390/w10091189
Limbrunner JF, Vogel RM, Chapra SC et al (2013) Classic optimization techniques applied to stormwater and nonpoint source pollution management at the watershed scale. J Water Resour Plan Manag 139(5):486–491. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000361
Liu J, Chen X (2019) An improved NSGA-II algorithm based on crowding distance elimination strategy. Int J Comput Intell Syst 12(2):513. https://doi.org/10.2991/ijcis.d.190328.001
Liu H, Mizzi S (2020) Evaluating climate changes and land use changes on water resources using hybrid soil and water assessment tool -DEEP optimized by metaheuristics. Concurr Comput. https://doi.org/10.1002/cpe.5945
Liu Y, Theller LO, Pijanowski BC et al (2016) Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: an application to the trail creek watershed, Indiana. Sci Total Environ 553:149–163. https://doi.org/10.1016/j.scitotenv.2016.02.116
Matos FA, Roebeling P (2022) Modelling impacts of nature-based solutions on surface water quality: a rapid review. Sustainability 14(12):7381. https://doi.org/10.3390/su14127381
Mayrhofer JP, Gupta J (2016) The science and politics of co-benefits in climate policy. Environ Sci Policy 57:22–30. https://doi.org/10.1016/j.envsci.2015.11.005
McKay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239. https://doi.org/10.2307/1268522
Merriman K, Russell A, Rachol C et al (2018) Calibration of a field-scale soil and water assessment tool (SWAT) model with field placement of best management practices in Alger creek, Michigan. Sustainability 10(3):851. https://doi.org/10.3390/su10030851
Meunier F (2022) Introduction à l’optimisation. Tech. rep, Ecole Nationale des Ponts et Chaussées (ENPC)
Montaseri M, Hesami Afshar M, Bozorg-Haddad O (2015) Development of simulation-optimization model (MUSIC-GA) for urban stormwater management. Water Resour Manage 29(13):4649–4665. https://doi.org/10.1007/s11269-015-1082-z
Ngatchou P, Zarei A, El-Sharkawi A (2006) Pareto multi objective optimization. In: Proceedings of the 13th international conference on, intelligent systems application to power systems. IEEE, 10.1109/ISAP.2005.1599245
Nievergelt J (2000) Exhaustive search, combinatorial optimization and enumeration: exploring the potential of raw computing power. In: Sofsem, Springer, 18–35, https://doi.org/10.1007/3-540-44411-4_2
Nirupama N, Simonovic SP (2007) Increase of flood risk due to urbanisation: a Canadian example. Nat Hazards (Dordr) 40(1):25–41. https://doi.org/10.1007/s11069-006-0003-0
Ogryczak W, Śliwiński T (2006) On direct methods for lexicographic min-max optimization. In: computational science and its applications - ICCSA 2006. Lecture notes in computer science, Springer, Berlin, Heidelberg, 802–811, https://doi.org/10.1007/11751595_85
ParadigmEnvironmental (2017) Developing reasonable assurance: a guide to performing model-based analysis to support municipal stormwater program planning. Tech. rep., Submitted to U.S. EPA
Parmentier A (2021) Operation research. Tech. rep, Ecole Nationale des Ponts et Chaussées (ENPC)
Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM symposium on applied computing. ACM, New York, NY, USA, https://doi.org/10.1145/508791.508907
Qi H, Altinakar MS, Vieira DAN et al (2008) Application of tabu search algorithm with a coupled AnnAGNPS-CCHE1D model to optimize agricultural land use. J Am Water Resour Assoc 44(4):866–878. https://doi.org/10.1111/j.1752-1688.2008.00209.x
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY) 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Raymond CM, Frantzeskaki N, Kabisch N et al (2017) A framework for assessing and implementing the co-benefits of nature-based solutions in urban areas. Environ Sci Policy 77:15–24. https://doi.org/10.1016/j.envsci.2017.07.008
Ritchie H, Rosado P, Roser M (2022) Natural disasters. Our World in Data Https://ourworldindata.org/natural-disasters
Rollett A, Rohrer GS, Hatherly M (2004) Recrystallization and related annealing phenomena, 2nd edn. Pergamon Press, London
Rosselló J, Becken S, Santana-Gallego M (2020) The effects of natural disasters on international tourism: a global analysis. Tour Manag 79(104):080. https://doi.org/10.1016/j.tourman.2020.104080
Rossman L (2007) Storm water management model user’s manual
Ruangpan L, Vojinovic Z, Di Sabatino S et al (2020) Nature-based solutions for hydro-meteorological risk reduction: a state-of-the-art review of the research area. Nat Hazards Earth Syst Sci 20(1):243–270. https://doi.org/10.5194/nhess-20-243-2020
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Serafini P (1994) Simulated annealing for multi objective optimization problems. In: multiple criteria decision making: proceedings of the tenth international conference: expand and enrich the domains of thinking and application, Springer, pp 283–292, https://doi.org/10.1007/978-1-4612-2666-6_29
SFEI (2015) Greenplan-it toolkit demonstration report. Tech. rep, San Francisco Estuary Institute
Sharp R, Tallis H, Ricketts T, et al (2016) Invest+ version+ user’s guide. The natural capital project
Shoemaker L, RiversonJr J, Alvi K et al (2009) A framework for placement of best management practices in urban watersheds to protect water quality. Tech. rep, United States Environmental Protection Agency (EPA)
Smith CS, Lejano RP, Ogunseitan OA et al (2007) Cost effectiveness of regulation-compliant filtration to control sediment and metal pollution in urban runoff. Environ Sci Technol 41(21):7451–7458. https://doi.org/10.1021/es062775z
Somarakis G, Stagakis S, Chrysoulakis N (2019) Thinknature nature-based solutions handbook. https://doi.org/10.26225/jerv-w202
Stagnitta T, Detenbeck N, Piscopo A (2018) An overview of the U.S. EP view of the U.S. EPA’s watershed management optimization support tool (wmost): a case study in t wmost): a case study in Taunton, Massachusetts. Tech. rep., Brigham Young University, U.S. EPA
Storn R, Price KV (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328
Tech T (2016) Opti-tool for stormwater and nutrient management (user’s guide). Prepared for United States Environmental Protection Agency-Region 1
TEEB (2010) The economics of ecosystems and biodiversity: Ecological and economic foundations. Earthscan, London and Washington
Thainiam P (2018) Developing tabu search with intensification and diversification for the seriation problem. In: 2018 5th International conference on industrial engineering and applications (ICIEA). IEEE, https://doi.org/10.1109/IEA.2018.8387110
Thorslund J, Jarsjo J, Jaramillo F et al (2017) Wetlands as large-scale nature-based solutions: status and challenges for research, engineering and management. Ecol Eng 108:489–497. https://doi.org/10.1016/j.ecoleng.2017.07.012
Veith TL, Wolfe ML, Heatwole CD (2004) Cost-effective bmp placement: optimization versus targeting. Trans ASAE 47(5):1585–1594. https://doi.org/10.13031/2013.17636
Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci USA 104(3):708–711. https://doi.org/10.1073/pnas.0610471104
Waltz F (1967) An engineering approach: hierarchical optimization criteria. IEEE Trans Autom Control 12(2):179–180. https://doi.org/10.1109/TAC.1967.1098537
Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408. https://doi.org/10.1007/s00500-016-2474-6
Wang S, Wang Y, Wang Y et al (2022) Comparison of multi-objective evolutionary algorithms applied to watershed management problem. J Environ Manag 324(116):255. https://doi.org/10.1016/j.jenvman.2022.116255
Wang Z, Pei Y, Li J (2023) A survey on search strategy of evolutionary multi-objective optimization algorithms. Appl Sci 13(7):4643. https://doi.org/10.3390/app13074643
Wheeler BW, Cooper AR, Page AS et al (2010) Greenspace and children’s physical activity: a GPS/GIS analysis of the PEACH project. Prev Med 51(2):148–152. https://doi.org/10.1016/j.ypmed.2010.06.001
Wilkerson GW, McAnally WH, Martin JL et al (2010) Latis: A spatial decision support system to assess low-impact site development strategies. Adv Civ Eng 2010:1–18. https://doi.org/10.1155/2010/810402
Wong THF, Fletcher TD, Duncan HP, et al (2002) A model for urban stormwater improvement: Conceptualization. In: Global solutions for urban drainage. American Society of Civil Engineers, Reston, VA, https://doi.org/10.1061/40644(2002)115
Yang S (2021) Assessment of trade-offs among the co-benefits of nature-based solutions for flood risk reduction. PhD thesis, IHE Delft Institute for Water Education, Delft
Yazdi J (2016) Decomposition based multi objective evolutionary algorithms for design of large-scale water distribution networks. Water Resour Manage 30(8):2749–2766. https://doi.org/10.1007/s11269-016-1320-z
Yazdi J, Salehi Neyshabouri SAA (2014) Identifying low impact development strategies for flood mitigation using a fuzzy-probabilistic approach. Environ Model Softw 60:31–44. https://doi.org/10.1016/j.envsoft.2014.06.004
Zadeh L (1963) Optimality and non-scalar-valued performance criteria. IEEE Trans Autom Control 8(1):59–60. https://doi.org/10.1109/TAC.1963.1105511
Zarpellon G, Jo J, Lodi A, et al (2020) Parameterizing branch-and-bound search trees to learn branching policies. In: Proceedings of the AAAI conference on artificial intelligence https://doi.org/10.48550/arXiv.2002.05120
Zhang K, Chui TFM (2018) A comprehensive review of spatial allocation of LID-BMP-GI practices: strategies and optimization tools. Sci Total Environ 621:915–929. https://doi.org/10.1016/j.scitotenv.2017.11.281
Zhang G, Hamlett JM, Reed P et al (2013) Multi-objective optimization of low impact development designs in an urbanizing watershed. Open J Optim 02(04):95–108. https://doi.org/10.4236/ojop.2013.24013
Acknowledgements
This review was conducted as part of an internship. Thanks to the help and supervision of Dr Felicien Barhebwa Mushamuka and the company Mitigrate for allowing me to discover this new field of application of optimisation and Operations Research.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
FBM and LF designed the review’s objectives, and FBM selected the first set of papers. JC read and analysed this set of documents and completed it with more specialised documents. JC produced the figures and then the first version of the paper. FBM corrected this version and added suggestions.
Corresponding author
Ethics declarations
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Appendices
Appendix A Case study examples
See Table 3.
Appendix B Article statistics
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Capgras, J., Barhebwa Mushamuka, F. & Feuilleaubois, L. Optimisation of selection and placement of nature-based solutions for climate adaptation: a literature review on the modelling and resolution approaches. Environ Syst Decis 43, 577–598 (2023). https://doi.org/10.1007/s10669-023-09933-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10669-023-09933-y