Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Abstract

This paper evaluates two established decision making methods and analyses their performance and suitability within a Water Resources Management (WRM) problem. The methods under assessment are Info-Gap decision theory (IG) and Robust Optimisation (RO). The methods have been selected primarily to investigate a contrasting local vs 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 analyses a set of prespecified strategies and the latter uses optimisation algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modelling and assesses the applicability of utilising the Future Flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study 2 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 costing 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.

A Resilience-Based Methodology for Improved Water Resources Adaptation Planning under Deep Uncertainty with Real World Application

“…The limitation of the current Future Flow projections is their utilization of only a medium global emission scenario; however, once resampled multiple times, the Future Flow projections provide an adequate range of uncertainty for this specific metric evaluation. Resampling of the flow projections (as outlined in Roach et al (2016)) eliminates any bias in the selection of adaptation strategies due to the timing and duration of future drought conditions exhibited, and enables a sufficient investigation into the role of climate variability on the region's resources.…”

“…A global robustness measure of satisficing performance utilizing pre-defined domain criterions has been selected for this study, as it elicits a transparent quantified calculation of robustness that is suitable when examining a wide range of highly variable discrete future scenarios and has been successfully employed in numerous recent WRM studies (Paton et al 2014;Beh et al 2015;Roach et al 2016). Robustness of long-term water supply is specifically defined here as in Roach et al (2016) as the fraction (i.e., percentage) of future supply and demand scenarios that result in an acceptable system performance (here in terms of resilience), as shown in Eq. (3).…”

“…Recent international water resources management (WRM) literature includes a wide array of contrasting approaches for planning under Bdeep^uncertainty, such as: Robust Decision Making (Matrosov et al 2013;Groves et al 2015), Info-Gap decision theory (Korteling et al 2013;Roach et al 2016), Decision Scaling (Brown et al 2012;Turner et al 2014) and Robust Optimization (Ray et al 2013;Kwakkel et al 2015). Most of these approaches have been developed to evaluate the performance of a decision or strategy by calculating system robustness, which is the term commonly used to describe the degree, or percentage of plausible future conditions, under which a water supply system maintains a satisfactory level of performance.…”

Inexact Multistage Stochastic Chance Constrained Programming Model for Water Resources Management under Uncertainties

“…Making the best use of limited water resources premises the sustainable development of economy and society [1]. In the past few decades, many researchers have applied optimization techniques [2][3][4][5][6][7] to deal with uncertainties in a number of system components and their interrelationships within water resources systems [8]. Among them, inexact multistage stochastic programming (IMSP) is regarded as a significant method for water resources management.…”

Multiobjective Analysis of Green-Blue Water Uses in a Highly Utilized Basin: Case Study of Pangani Basin, Africa

“…These studies have used a range of deterministic and stochastic -single to several objective problem formulations. In recent studies, advanced multi-objective optimization algorithms that rely on pareto-optimal curves or surfaces have been developed (Kasprzyk et al 2009; Kollat et al 2011;Fu et al 2012;Reed et al 2013;Marton and Kapelan 2014;Chu et al 2015;Roach et al 2016). …”