A DSS for water resources management under uncertainty by scenario analysis

Abstract

Abstract:In this paper we present a scenario analysis approach to perform water system planning and management under climatic and hydrological uncertainty. A DSS with a a graphical interface allows the user a friendly data-input phase and results analysis. Different generation techniques can be used to set up and analyze a number of scenarios. Uncertainty is modeled by a scenario-tree in a multistage environment, which includes different possible configurations of inflows in a wide time-horizon. The aim is to identify trends and essential features on which to base a robust decision policy. The DSS prevent obsolescence of optimizer codes exploiting the standard input format MPS. Obtained results show that scenario analysis could be an alternative approach to stochastic optimization when no probabilistic rules can be adopted and deterministic models are inadequate to represent uncertainty. Moreover, experimentation to a real water resources system in Sardinia, Italy, shows that the DSS can be easily used by practitioners and end-users.

A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty

“…Scenario-based optimisation has been used successfully in several cases to make decisions while taking into consideration possible future scenarios (see, for example, Ramos, 1992, Morton, 1996, Pallotino et al, 2002, Li et al, 2003, Mulvey et al, 2004and Beraldi et al, 2006.…”

Exploiting Qualitative Information for Decision Support in Scenario Analysis

“…Their studies included "scenario as a decision-support component of the DSS and defines scenario-driven DSS as an interactive computer-based system, which integrates diverse data, models and solvers to explore decision scenarios for supporting the decision makers in solving problems" (Ahmed and Sundaram, 2008; page 46). Pallottino et al (2005) presented a scenario-modelling framework to solve water-system optimisation problems, as an alternative to the traditional stochastic approach. Based on SA approaches for water resources proposed in these former studies, the water resource SA tool was proposed to evolve into a DSS that allows the achieving of a 'robust' decision policy that should minimise the risk of incorrect decisions: "in the proposed decision support system, the availability of an efficient computer graphical interface helps end-users to evaluate with ease the best choice and to reach a robust solution" (Pallottino et al, 2005(Pallottino et al, , page 1032).…”

“…Pallottino et al (2005) presented a scenario-modelling framework to solve water-system optimisation problems, as an alternative to the traditional stochastic approach. Based on SA approaches for water resources proposed in these former studies, the water resource SA tool was proposed to evolve into a DSS that allows the achieving of a 'robust' decision policy that should minimise the risk of incorrect decisions: "in the proposed decision support system, the availability of an efficient computer graphical interface helps end-users to evaluate with ease the best choice and to reach a robust solution" (Pallottino et al, 2005(Pallottino et al, , page 1032). In the Global Environmental Change and Food Systems (GECAFS) project, one of the research objectives is the development of DSS to assist decision makers in the science-policy-practice dialogue.…”

The Porting of Wargi-DSS to Grid Computing Environment for Drought Plans in Complex Water Systems

“…Specific procedures in WARGI manage the dynamic link with the solver and allow to select the solving code. If analysis with scenario optimization is required, WARGI-SCEN module passes to the WARGI-OPT module (Pallottino et al, 2005) parameters for model construction to consider different system evolutions. Moreover, water quality optimization considering synthetic quality indexes for water sources and demands is implemented in the WARGI-QUAL module Sulis, 2007b, 2009b).…”