Fariba Babaeian, Majid Delavar, Saeed Morid and Shervin Jamshidi, 2023, Designing climate change dynamic adaptive policy pathways for agricultural water management using a socio-hydrological modeling approach, Journal of Hydrology, Volume 627, Part A, December 2023, 130398. Abstract.
Mirzaei, Mohammad Amin, Mehrjerdi, Hassan, and Mansour Saatloo, Amin, 2023, Look-ahead scheduling of energy-water nexus integrated with Power2X conversion technologies under multiple uncertainties, Sustainable Cities and Society, vol. 99, Dec. 2023, Article number 104902. Abstract.
Maria Mavrova-Guirguinova, Julieta Mancheva and Denislava Pencheva, 2023, Decision analysis for robust long-term flood management: Uncertainty exploration using probabilistic approach and information-gap decision theory, International Journal of Design and Nature and Ecodynamics, Vol.18, Issue 1, Pages 117–124, February 2023. Abstract.
Boindala Sriman Pankaj, G. Jaykrishnan and Avi Ostfeld, 2022, Optimizing water quality treatment levels for water distribution systems under mixing uncertainty at junctions, Journal of Water Resources Planning and Management, Volume 148, Issue 5, May 2022, DOI: 10.1061/(ASCE)WR.1943-5452.0001544. Abstract.
Thomas van der Pol, Jochen Hinkel, Jan Merkens, Leigh MacPherson, Athanasios T. Vafeidis, Arne Arnse, Sonke Dangendorff, 2021, Regional economic analysis of flood defence heights at the German Baltic Sea coast: A multi-method cost-benefit approach for flood prevention, February 2021, Climate Risk Management, 32(04016028): 100289, DOI: 10.1016/j.crm.2021.100289. Abstract.
Fariba Babaeian, Majid Delavar, Saeed Morid and Raghavan Srinivasan, 2021, Robust climate change adaptation pathways in agricultural water management, June 2021, Agricultural Water Management, 252(3): 106904, DOI: 10.1016/j.agwat.2021.106904. Abstract.
Housh, M.; Aharon, T., 2021, Info-gap models for optimal multi-year management of regional water resources systems under uncertainty, Sustainability, 2021, 13, 3152. Abstract.
Ranjbar, A. and Cherubini, C., 2020, Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer), Heliyon, Volume 6, Issue 12, December 2020, Article number e05758. Abstract.
Ali Ranjbar and Najmeh Mahjouri, 2019, Multi-objective freshwater management in coastal aquifers under uncertainty in hydraulic parameters, Natural Resources Research, DOI: 10.1007/s11053-019-09585-3. Abstract.
Maryam Soltani, Reza Kerachian, Mohammad reza Nikoo, Hamideh Noory, 2018, Planning for agricultural return flow allocation: Application of info-gap decision theory and a nonlinear CVaR-based optimization model, Environmental Science and Pollution Research, DOI: 10.1007/s11356-018-2544-7. Abstract.
Ahmed E. Al-Juaidi and Tarek Hegazy, 2017, Conflict resolution for Sacramento-San-Joaquin delta with stability and sensitivity analyses using the graph model, British Journal of Mathematics & Computer Science, vol. 20 (5): 1-10, DOI: 10.9734/BJMCS/2017/31225. Abstract.
Ahmed E. M. Al-Juaidi, 2017, Decision support system analysis with the graph model on non-cooperative generic water resource conflicts, International Journal of Engineering & Technology, 6 (4): 145-153. Abstract.
Ahmed E. Al-Juaidi, 2017, Decision support system with multi-criteria, stability, and uncertainty analysis for resolving the municipal infrastructure conflict in the city of Jeddah, Journal of King Saud University – Engineering Sciences, to appear. Abstract.
Ghodsi, S.H., Kerachian, R., Estalaki, S.M., Nikoo, M.R., Zahmatkesh, Z., 2016, Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories, Journal of Hydrology, Vol. 533: 200-212.
In this paper, two deterministic and stochastic multilateral, multi-issue, non-cooperative bargaining methodologies are proposed for urban runoff quality management. In the proposed methodologies, a calibrated Storm Water Management Model (SWMM) is used to simulate stormwater runoff quantity and quality for different urban stormwater runoff management scenarios, which have been defined considering several Low Impact Development (LID) techniques. In the deterministic methodology, the best management scenario, representing location and area of LID controls, is identified using the bargaining model. In the stochastic methodology, uncertainties of some key parameters of SWMM are analyzed using the info-gap theory. For each water quality management scenario, robustness and opportuneness criteria are determined based on utility functions of different stakeholders. Then, to find the best solution, the bargaining model is performed considering a combination of robustness and opportuneness criteria for each scenario based on utility function of each stakeholder. The results of applying the proposed methodology in the Velenjak urban watershed located in the northeastern part of Tehran, the capital city of Iran, illustrate its practical utility for conflict resolution in urban water quantity and quality management. It is shown that the solution obtained using the deterministic model cannot outperform the result of the stochastic model considering the robustness and opportuneness criteria. Therefore, it can be concluded that the stochastic model, which incorporates the main uncertainties, could provide more reliable results.
Yakov Ben-Haim, Xavier Irias and Roberts McMullin, 2015, Managing technological and economic uncertainties in design of long-term infrastructure projects: An info-gap approach, 25th CIRP Design Conference, Procedia CIRP, 36 pp. 59-63, Haifa, Israel. Pre-print.
Jonathan D. Herman, Patrick M. Reed, Ph.D., Harrison B. Zeff, and Gregory W. Characklis, 2015, How should robustness be defined for water systems planning under change? Journal of Water Resources Planning and Management, vol. 141, issue 10. Abstract.
Jonatan Zischg, Mariana L. R. Goncalves, Taneha Kuzniecow Bacchin, Guenther Leonhardt, Maria Viklander, Arjan van Timmeren, Wolfgang Rauch and Robert Sitzenfrei, 2017, Info-Gap robustness pathway method for transitioning of urban drainage systems under deep uncertainties, Water Science & Technology, 76(5): 1272-1281. Abstract.
Xavier Irias, Robustness: Strategies for Utility Management in Conditions of Uncertainty, Source, vol.26, #2, pp.20-23, Spring 2012. Online version.
D.V. Cicala and X. Irias, 2014, Utilizing info-gap decision theory to improve pipeline reliability: A case study,Pipelines 2014: From Underground to the Forefront of Innovation and Sustainability, Portland, 3-6 August 2014, pp.1749-1760.
T. Roach, Z. Kapelan, R. Ledbetter and M. Ledbetter, 2016, Comparison of robust optimization and info-gap methods for water resource management under deep uncertainty, Journal of Water Resources Planning and Management, 142: 9, art. no. 08217002.
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.
Tom Roach, Zoran Kapelan and Ralph Ledbetter, 2015, Comparison of info-gap and robust optimisation methods for integrated water resource management under severe uncertainty, Procedia Engineering, 119 (2015) 874-883, 13th Computer Control for Water Industry Conference, CCWI 2015.
This paper evaluates two established decision making methods and analyses their performance and suitability within an Integrated Water Resource Management (IWRM) problem. The methods under comparison are Info-Gap decision theory (IG) and Robust Optimisation (RO), with particular regard to two key issues: (a) a local vs global measure of water supply robustness and (b) a pre-specified vs optimisation method of generating intervention strategies. Solutions are compared with plans proposed from current industry practice especially in regard to employing a longer planning horizon. The results reveal the impact of using alternative methodologies and analysis parameters on the final intervention strategies selected.
Water resources planning, decision making methods, climate change uncertainity, robust optimisation, info-gap decision theory
Korteling, B., Dessai, S., Kapelan, Z., 2012, Using information-gap decision theory for water resources planning under severe uncertainty, Water Resources Management, 27 (4): 1149-1172.
Water resource managers are required to develop comprehensive water resources plans based on severely uncertain information of the effects of climate change on local hydrology and future socio-economic changes on localised demand. In England and Wales, current water resources planning methodologies include a headroom estimation process separate from water resource simulation modelling. This process quantifies uncertainty based on only one point of an assumed range of deviations from the expected climate and projected demand 25 years into the future. This paper utilises an integrated method based on Information-Gap decision theory to quantitatively assess the robustness of various supply side and demand side management options over a broad range of plausible futures. Findings show that beyond the uncertainty range explored with the headroom method, a preference reversal can occur, i.e. some management options that underperform at lower uncertainties, outperform at higher levels of uncertainty. This study also shows that when 50% or more of the population adopts demand side management, efficiency related measures and innovative options such as rainwater collection can perform equally well or better than some supply side options The additional use of Multi-Criteria Decision Analysis shifts the focus away from reservoir expansion options, that perform best in regards to water availability, to combined strategies that include innovative demand side management actions of rainwater collection and greywater reuse as well efficiency measures and additional regional transfers. This paper illustrates how an Information-Gap based approach can offer a comprehensive picture of potential supply/demand futures and a rich variety of information to support adaptive management of water systems under severe uncertainty.
Climate change; Demand management; Info-Gap; Planning; Uncertainty; Water resources.
Matthew Grasinger, Daniel O’Malley, Velimir Vesselinov, and Satish Karra, 2016, Decision analysis for robust CO2 injection: Application of Bayesian-Information-Gap Decision Theory, International Journal of Greenhouse Gas Control, 49: 73-80. Abstract.
O’Malley, D. and Vesselinov, V.V., 2014, Groundwater remediation using the information gap decision theory,Water Resources Research, 50(1): 246-256.
One of the challenges in the design and selection of remediation activities for subsurface contamination is dealing with manifold uncertainties. A scientifically defensible decision process demands consideration of the uncertainties involved. A nonprobabilistic approach based on information gap (info-gap) decision theory is employed to study the robustness of alternative remediation activities. This approach incorporates both parametric and nonparametric (conceptual) uncertainty in predicting contaminant concentrations that are effected by natural processes and the remediation activities. Two remedial scenarios are explored to demonstrate the applicability of the info-gap approach to decision making related to groundwater remediation.
Dylan R. Harp, Curtis M. Oldenburg and Rajesh Pawar, 2019, A Metric for Evaluating Conformance Robustness During Geologic CO2 Sequestration Operations, International Journal of Greenhouse Gas Control, to appear. Abstract.
Dylan R. Harp, Philip H. Stauffer, Daniel O’Malley, Zunsheng Jiao, Evan P. Egenolf, Terry A. Miller, Daniella Martinez, Kelsey A. Hunter, Richard S. Middleton, Jeffrey M. Bielicki, Rajesh Pawar, 2017, Development of robust pressure management strategies for geologic CO2 sequestration, International Journal of Greenhouse Gas Control, vol. 64, September 2017, Pages 43-59. Abstract.
Dylan R. Harp and Velimir V. Vesselinov, 2013, Contaminant remediation decision analysis using information gap theory, Stochastic Environmental Research and Risk Assessment,27(1) pp.159-168. Abstract and full paper.
O’Malley, D. and Vesselinov, V.V., 2016, Bayesian-information-gap decision theory with an application to CO2 sequestration, Water Resources Research, vol. 49, pp.73-80. DOI: 10.1002/2015WR017413. The full paper.
Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and nonprobabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information-Gap Decision Theory (IGDT) to address model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real-world engineering problems related to the enumeration of possible outcomes. In the case of zero nonprobabilistic uncertainty, the method reduces to a Bayesian method. To illustrate the approach, we apply it to a site-selection decision for geologic CO2 sequestration.
Ashley Woods, Evgenii Matrosov, Julien J. Harou, 2011, Applying info-gap decision theory to water supply system planning: Application to the thames basin, Computer Control and the Water Industry (CCWI) Conference, Exeter, UK, Sept 2011. Pre-print.
Matrosov, E.S., Woods, A.M., Harou, J.J., 2013, Robust decision making and info-gap decision theory for water resource system planning, Journal of Hydrology, 2013, 494: 43-58.
Stationarity assumptions of linked human-water systems are frequently invalid given the difficult-to-predict changes affecting such systems. In this case water planning occurs under conditions of deep or severe uncertainty, where the statistical distributions of future conditions and events are poorly known. In such situations predictive system simulation models are typically run under different scenarios to evaluate the performance of future plans under different conditions. Given that there are many possible plans and many possible futures, which simulations will lead to the best designs? Robust Decision Making (RDM) and Info-Gap Decision Theory (IGDT) provide a structured approach to planning complex systems under such uncertainty. Both RDM and IGDT make repeated use of trusted simulation models to evaluate different plans under different future conditions. Both methods seek to identify robust rather than optimal decisions, where a robust decision works satisfactorily over a broad range of possible futures. IGDT efficiently charts system performance with robustness and opportuneness plots summarising system performance for different plans under the most dire and favourable sets of future conditions. RDM samples a wider range of dire, benign and opportune futures and offers a holistic assessment of the performance of different options. RDM also identifies through ‘scenario discovery’ which combinations of uncertain future stresses lead to system vulnerabilities. In our study we apply both frameworks to a water resource system planning problem: London’s water supply system expansion in the Thames basin, UK. The methods help identify which out of 20 proposed water supply infrastructure portfolios is the most robust given severely uncertain future hydrological inflows, water demands and energy prices. Multiple criteria of system performance are considered: service reliability, storage susceptibility, capital and operating cost, energy use and environmental flows. Initially the two decision frameworks lead to different recommendations. We show the methods are complementary and can be beneficially used together to better understand results and reveal how the particulars of each method can skew results towards particular future plans.
Info-Gap Decision Theory (IGDT); Infrastructure planning; Robust Decision Making (RDM); Uncertainty; Water resources planning
Hine, Daniel and Jim W. Hall, 2010, Information gap analysis of flood model uncertainties and regional frequency analysis, Water Resources Research, vol. 46, issue 1, W01514, doi:10.1029/2008WR007620.
Flood risk analysis is subject to often severe uncertainties, which can potentially undermine flood management decisions. This paper explores the use of information gap theory to analyze the sensitivity of flood management decisions to uncertainties in flood inundation models and flood frequency analysis. Information gap is a quantified nonprobabilistic theory of robustness. To analyze uncertainties in flood modeling, an energy-bounded information gap model is established and applied first to a simplified uniform channel and then to a more realistic 2-D flood model. Information gap theory is then applied to the estimation of flood discharges using regional frequency analysis. The use of an information gap model is motivated by the notion that hydrologically similar sites are clustered in the space of their L moments. The information gap model is constructed around a parametric statistical flood frequency analysis, resulting in a hybrid model of uncertainty in which natural variability is handled statistically while epistemic uncertainties are represented in the information gap model. The analysis is demonstrated for sites in the Trent catchment, United Kingdom. The analysis is extended to address ungauged catchments, which, because of the attendant uncertainties in flood frequency analysis, are particularly appropriate for information gap analysis. Finally, the information gap model of flood frequency is combined with the treatment of hydraulic model uncertainties in an example of how both sources of uncertainty can be accounted for using information gap theory in a flood risk management decision.
Hall, J.W. and Harvey, H., 2009, Decision making under severe uncertainty for flood risk management: A case study of info-gap robustness analysis. Proc. Int. Conf. Science and Information Technologies for Sustainable Management of Aquatic Ecosystems, Concepcion, Chile, 12-16 January 2009.
University of Newcastle upon Tyne and Halcrow. Early Conceptual Options, A framework for Uncertainty Analysis in TE2100, Environment Agency ECO Report R10X, June 2006, 42pp.
Jim Hall and Dimitri Solomatine, 2008, A framework for uncertainty analysis in flood risk management decisions, International Journal of River Basin Management, Vol.6, No. 2, pp.85–98.
D.J. Hine and J.W. Hall, Convex Analysis of Flood Inundation Model Uncertainties and Info-Gap Flood Management Decisions, ISSH – Stochastic Hydraulics 2005, 23 and 24 May 2005, Nijmegen, The Netherlands.
Keith W. Hipel and Yakov Ben-Haim, 1999, Decision making in an uncertain world: Information-gap modelling in water resources management, IEEE Trans., Systems, Man and Cybernetics, Part C: Applications and Reviews, 29: 506-517. Pre-print.