Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty
This article has been corrected.
VIEW CORRECTIONPublication: Journal of Water Resources Planning and Management
Volume 142, Issue 9
Abstract
This paper evaluates two established decision-making methods and analyzes their performance and suitability within a water resources management (WRM) problem. The methods under assessment are info-gap (IG) decision theory and robust optimization (RO). The methods have been selected primarily to investigate a contrasting local versus global method of assessing water system robustness to deep uncertainty, but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyzes a set of prespecified strategies and the latter uses optimization algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modeling and assesses the applicability of utilizing the future flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower cost strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was financially supported by the UK Engineering and Physical Sciences Research Council, HR Wallingford and The University of Exeter through the STREAM Industrial Doctorate Centre. The authors are grateful to Dr Steven Wade, now at the Met Office, and Chris Counsell of HR Wallingford for providing data for the Sussex North case study.
References
Arnell, N. W., and Reynard, N. S. (1996). “The effects of climate change due to global warming on river flows in Great Britain.” J. Hydrol., 183(3–4), 397–424.
Atkins. (2007). “Sussex North water resource investigations—Phase 3 options appraisal report.”, Epsom.
Bai, D., Carpenter, T., and Mulvey, J. (1997). “Making a case for robust optimization models.” Manage. Sci., 43(7), 895–907.
Bankes, S. (2011). The use of complexity for policy exploration, SAGE Publications, London.
Beh, E. H. Y., Maier, H. R., and Dandy, G. C. (2015a). “Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty.” Water Resour. Res., 51(3), 1529–1551.
Beh, E. H. Y., Maier, H. R., and Dandy, G. C. (2015b). “Scenario driven optimal sequencing under deep uncertainty.” Environ. Modell. Softw., 68(3), 181–195.
Ben-Haim, Y. (2001). “Decision trade-offs under severe info-gap uncertainty.” 2nd Int. Symp. on Imprecise Probabilities and Their Applications, Vol. 4, Ithaca, NY.
Ben-Haim, Y. (2006). Info-gap decision theory: Decisions under severe uncertainty, 2nd Ed., Academic Press, New York.
Ben-Haim, Y. (2010). Info-gap economics: An operational introduction, Palgrave Macmillan, Basingstoke, U.K.
Ben-Tal, A., El Ghaoui, L., and Nemirovski, A. (2009). Robust optimization, Princeton University Press, Princeton, NJ.
Ben-Tal, A., and Nemirovski, A. (1998). “Robust convex optimization.” Math. Oper. Res., 23(4), 769–805.
Ben-Tal, A., and Nemirovski, A. (2000). “Robust solutions of linear programming problems contaminated with uncertain data.” Math. Program., 88(3), 411–424.
Bertsimas, D., and Sim, M. (2004). “The price of robustness.” Oper. Res., 52(1), 35–53.
Bjorklund, G. (2001). “Water management in developing countries—Policy and priorities for EU development cooperation.”, Stockholm, Sweden.
Borgomeo, E., Hall, J. W., Fung, F., Watts, G., Colquhoun, K., and Lambert, C. (2014). “Risk-based water resources planning: Incorporating probabilistic nonstationary climate uncertainties.” Water Resour. Res., 50(8), 6850–6873.
Borgwardt, K.-H. (1987). The simplex algorithm: A probabilistic analysis—Algorithms and combinatorics, 1st Ed., Springer, Berlin.
Brown, C. (2010). “Decision-scaling for robust planning and policy under climate uncertainty.”, Evidence on Demand, Washington, DC.
Brown, C., and Baroang, K. M. (2011). “Risk assessment, risk management, and communication: Methods for climate variability and change.” P. A. Wilderer, ed., Vol. 1, Treatise on water science, Elsevier, New York, 189–199.
Bryant, B. P., and Lempert, R. J. (2010). “Thinking inside the box: A participatory, computer-assisted approach to scenario discovery.” Technol. Forecast. Soc., 77(1), 34–49.
Charlton, M. B., and Arnell, N. W. (2011). “Adapting to climate change impacts on water resources in England—An assessment of draft water resources management plans.” Global Environ. Change, 21(1), 238–248.
Coello, C. A. C. (1999). “A comprehensive survey of evolutionary-based multiobjective optimization techniques.” Knowledge Inf. Syst., 1(3), 269–308.
Deb, K., and Gupta, H. (2006). “Introducing robustness in multi-objective optimization.” Evol. Comput., 14(4), 463–494.
Deb, K., and Pratap, A. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput., 6(2), 182–197.
DEFRA (Department for Environment, Food and Rural Affairs). (2013). “Updating the general duties with respect to the water industry to reflect the UK Government’s resilience priorities.” London.
Diaz-Nieto, J., and Wilby, R. L. (2005). “A comparison of statistical downscaling and climate change factor methods: Impacts on low flows in the River Thames, United Kingdom.” Clim. Change, 69(2–3), 245–268.
Dorigo, M., Maniezzo, V., and Colorni, A. (1996). “Ant system: Optimization by a colony of cooperating agents.” IEEE Trans. Syst. Man Cybern. Part B Cybern., 26(1), 29–41.
Environment Agency. (2007). “Areas of water stress: Final classification.” Bristol, U.K.
Environment Agency. (2013). “Water resources management plans 2019—Preparing for the future (main report).” Halcrow Group, Swindon, U.K.
Environment Agency, OFWAT, DEFRA, and Welsh Government. (2012). “Water resources planning guideline—The technical methods and instructions.” Bristol, U.K.
Eusuff, M. M., and Lansey, K. E. (2003). “Optimization of water distribution network design using the shuffled frog leaping algorithm.” J. Water Resour. Plann. Manage., 210–225.
Falkenmark, M., and Folke, C. (2000). “How to bring ecological services into integrated water resources management.” AMBIO J. Hum. Environ., 29(6), 351–352.
Frank, M., and Wolfe, P. (1956). “An algorithm for quadratic programming.” Nav. Res. Log. Q., 3(1–2), 95–110.
Fu, G., Kapelan, Z., Kasprzyk, J. R., and Reed, P. (2013). “Optimal design of water distribution systems using many-objective visual analytics.” J. Water Resour. Plann. Manage., 624–633.
Geoffrion, A. M., Dyer, J. S., and Feinberg, A. (1972). “An interactive approach for multi-criterion optimization, with an application to the operation of an academic department.” Manage. Sci., 19(4), 357–368.
Ghile, Y. B., Taner, M. Ü., Brown, C., Grijsen, J. G., and Talbi, A. (2014). “Bottom-up climate risk assessment of infrastructure investment in the Niger river basin.” Clim. Change, 122(1–2), 97–110.
Giuliani, M., Herman, J. D., Castelletti, A., and Reed, P. (2014). “Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management.” Water Resour. Res., 50(4), 3355–3377.
Groves, D. G., Yates, D., and Tebaldi, C. (2008). “Developing and applying uncertain global climate change projections for regional water management planning.” Water Resour. Res., 44(12), W12413.
Haasnoot, M., Kwakkel, J. H., Walker, W. E., and Ter Maat, J. (2013). “Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world.” Global Environ. Change, 23(2), 485–498.
Hadka, D., and Reed, P. (2013). “Borg: An auto-adaptive many-objective evolutionary computing framework.” Evol. Comput., 21(2), 231–259.
Hall, J. W., et al. (2012a). “Towards risk-based water resources planning in England and Wales under a changing climate.” Water Environ. J., 26(1), 118–129.
Hall, J. W., Lempert, R. J., Keller, K., Hackbarth, A., Mijere, C., and McInerney, D. J. (2012b). “Robust climate policies under uncertainty: A comparison of robust decision making and info-gap methods.” Risk Anal., 32(10), 1657–1672.
Hamarat, C., Kwakkel, J. H., and Pruyt, E. (2013). “Adaptive robust design under deep uncertainty.” Technol. Forecast. Soc., 80(3), 408–418.
Hashimoto, T., Stedinger, J. R., and Loucks, D. P. (1982). “Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation.” Water Resour. Res., 18(1), 14–20.
Herman, J. D., Zeff, H. B., Reed, P. M., and Characklis, G. W. (2014). “Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty.” Water Resour. Res., 50(10), 7692–7713.
Hipel, K. W., and Ben-Haim, Y. (1999). “Decision making in an uncertain world: Information-gap modeling in water resources management.” IEEE Trans. Syst. Man. Cybern. Part C Appl. Rev., 29(4), 506–517.
IPCC (Intergovernmental Panel on Climate Change). (2007). “Climate change 2007: The physical science basis.” Cambridge University Press, Cambridge, U.K.
Jeuland, M., and Whittington, D. (2014). “Water resources planning under climate change: Assessing the robustness of real options for the Blue Nile.” Water Resour. Res., 50(3), 2086–2107.
Kang, D., and Lansey, K. (2013). “Scenario-based robust optimization of regional water and wastewater infrastructure.” J. Water Resour. Plann. Manage., 325–338.
Kang, D., and Lansey, K. (2014). “Multi-period planning of water supply infrastructure based on scenario analysis.” J. Water Resour. Plann. Manage., 40-54.
Kasprzyk, J. R., Reed, P. M., Characklis, G. W., and Kirsch, B. R. (2012). “Many-objective de Novo water supply portfolio planning under deep uncertainty.” Environ. Modell. Software, 34, 87–104.
Kollat, J. B., and Reed, P. M. (2006). “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design.” Adv. Water Resour., 29(6), 792–807.
Korteling, B., Dessai, S., and Kapelan, Z. (2013). “Using information-gap decision theory for water resources planning under severe uncertainty.” Water Resour. Manage., 27(4), 1149–1172.
Kwakkel, J., Haasnoot, M., and Walker, W. (2015). “Developing dynamic adaptive policy pathways: A computer-assisted approach for developing adaptive strategies for a deeply uncertain world.” Clim. Change, 132(3), 373–386.
Ledbetter, R., Prudhomme, C., and Arnell, N. (2012). “A method for incorporating climate variability in climate change impact assessments: Sensitivity of river flows in the Eden catchment to precipitation scenarios.” Clim. Change, 113(3–4), 803–823.
Lempert, R. J., and Collins, M. T. (2007). “Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches.” Risk Anal., 27(4), 1009–1026.
Lempert, R. J., and Groves, D. G. (2010). “Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west.” Technol. Forecast. Soc., 77(6), 960–974.
Li, Y. P., Huang, G. H., and Nie, S. L. (2009). “A robust interval-based minimax-regret analysis approach for the identification of optimal water-resources-allocation strategies under uncertainty.” Resour. Conserv. Recycl., 54(2), 86–96.
Mahmoud, M., et al. (2009). “A formal framework for scenario development in support of environmental decision-making.” Environ. Modell. Software, 24(7), 798–808.
Maier, H. R., et al. (2014). “Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions.” Environ. Modell. Software, 62, 271–299.
Matrosov, E. S., Woods, A. M., and Harou, J. J. (2013). “Robust decision making and info-gap decision theory for water resource system planning.” J. Hydrol., 494, 43–58.
Milly, P. C. D., et al. (2008). “Climate change. Stationarity is dead: Whither water management?” Science, 319(5863), 573–574.
Moody, P., and Brown, C. (2013). “Robustness indicators for evaluation under climate change: Application to the upper Great Lakes.” Water Resour. Res., 49(6), 3576–3588.
Nazemi, A., Wheater, H. S., Chun, K. P., and Elshorbagy, A. (2013). “A stochastic reconstruction framework for analysis of water resource system vulnerability to climate-induced changes in river flow regime.” Water Resour. Res., 49(1), 291–305.
NERA (National Economic Research Associates). (2002). “The economics of balancing supply and demand (EBSD) main report.” London.
Nicklow, J., et al. (2010). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage., 412–432.
Paton, F. L., Dandy, G. C., and Maier, H. R. (2014a). “Integrated framework for assessing urban water supply security of systems with non-traditional sources under climate change.” Environ. Modell. Software, 60, 302–319.
Paton, F. L., Maier, H. R., and Dandy, G. C. (2014b). “Including adaptation and mitigation responses to climate change in a multiobjective evolutionary algorithm framework for urban water supply systems incorporating GHG emissions.” Water Resour. Res., 50(8), 6285–6304.
Prudhomme, C., et al. (2012). “Future flows climate.” 〈https://doi.org/10.5285/bad1514f-119e-44a4-8e1e-442735bb9797〉 (Feb. 10, 2015).
Python version 2.7 [Computer software]. Python Software Foundation, Amsterdam, Netherlands.
Ray, P. A., and Brown, C. (2015). Confronting climate uncertainty in water resources planning and project design: The decision tree framework, World Bank Publications, Washington, DC.
R Core Team. (2013). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria.
Reed, P. M., and Kollat, J. B. (2013). “Visual analytics clarify the scalability and effectiveness of massively parallel many-objective optimization: A groundwater monitoring design example.” Adv. Water Resour., 56, 1–13.
Rosenhead, J., Elton, M., and Guta, S. (1972). “Robustness and optimality as criteria for strategic decisions.” J. Oper. Res. Soc., 23(2), 413–4341.
Singh, R., Wagener, T., Crane, R., Mann, M. E., and Ning, L. (2014). “A vulnerability driven approach to identify adverse climate and land use change combinations for critical hydrologic indicator thresholds: Application to a watershed in Pennsylvania, USA.” Water Resour. Res., 50(4), 3409–3427.
Sniedovich, M. (2007). “The art and science of modeling decision-making under severe uncertainty.” Dec. Making Manuf. Serv., 1(1–2), 111–136.
Sniedovich, M. (2012). “Fooled by local robustness.” Risk Anal., 32(10), 1630–1637.
Southern Water. (2009). “Water resources management plan 2010–2035.” 〈https://www.southernwater.co.uk/〉 (Oct. 3, 2014).
Southern Water. (2014). “Water resources management plan 2015–2040.” 〈https://www.southernwater.co.uk/〉 (Feb. 10, 2015).
Tingstad, A., Groves, D., and Lempert, R. (2014). “Paleoclimate scenarios to inform decision making in water resource management: Example from Southern California’s inland empire.” J. Water Resour. Plann. Manage., 04014025.
Turner, S. W. D., Blackwell, R. J., Smith, M. A., and Jeffrey, P. J. (2014a). “Risk-based water resources planning in England and Wales: Challenges in execution and implementation.” Urban Water J., 13(2), 1–16.
Turner, S. W. D., Marlow, D., Ekström, M., Rhodes, B. G., Kularathna, U., and Jeffrey, P. J. (2014b). “Linking climate projections to performance: A yield-based decision scaling assessment of a large urban water resources system.” Water Resour. Res., 50(4), 3553–3567.
UKWIR (UK Water Industry Research). (1998). “A practical method for converting uncertainty into headroom.”, London.
Walker, W., Haasnoot, M., and Kwakkel, J. (2013a). “Adapt or perish: A review of planning approaches for adaptation under deep uncertainty.” Sustainability, 5(3), 955–979.
Walker, W. E., Lempert, R. J., and Kwakkel, J. H. (2013b). “Deep uncertainty.” Encyclopedia of operational research and management science, 3rd Ed., S. Gass and M. Fu, eds., Springer, Berlin, 395–402.
Weng, S. Q., Huang, G. H., and Li, Y. P. (2010). “An integrated scenario-based multi-criteria decision support system for water resources management and planning—A case study in the Haihe River basin.” Exp. Syst. Appl., 37(12), 8242–8254.
Whateley, S., Steinschneider, S., and Brown, C. (2014). “A climate change range-based method for estimating robustness for water resources supply.” Water Resour. Res., 50(11), 8944–8961.
Woods, A., Matrosov, E., and Harou, J. J. (2011). “Applying Info-Gap decision theory to water supply system planning: Application to the Thames basin.” Proc., Computer Control in the Water Industry (CCWI) 2011, Exeter, U.K.
Yazdani, A., Otoo, R. A., and Jeffrey, P. (2011). “Resilience enhancing expansion strategies for water distribution systems: A network theory approach.” Environ. Modell. Software, 26(12), 1574–1582.
Zarghami, M., and Hajykazemian, H. (2013). “Urban water resources planning by using a modified particle swarm optimization algorithm.” Resour. Conserv. Recycl., 70, 1–8.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 142 • Issue 9 • September 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: May 12, 2015
Accepted: Jan 22, 2016
Published online: May 3, 2016
Published in print: Sep 1, 2016
Discussion open until: Oct 3, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.