Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events

Abstract

The availability and the quality of drinking water are key requirements for the well-being and the safety of a community, both in ordinary conditions and in case of disasters. Providing safe drinking water in emergency contributes to limit the intensity and the duration of crises, and is thus one of the main concerns for decision-makers, who operate under significant uncertainty. The present work proposes a Decision Support System for the emergency management of drinking water supply systems, integrating: i) a vulnerability assessment model based on Bayesian Belief Networks with the related uncertainty assessment model; ii) a model for impact, and related uncertainty assessment, based on Bayesian Belief Networks. The results of these models are jointly analyzed, providing decision-makers with a ranking of the priority of intervention. A GIS interface (G-Net) is developed to manage both input spatial information and results. The methodology is implemented in L'Aquila case study, discussing the potentialities associated to the use of the tool dealing with information and data uncertainty.

Dynamic Models for Exploring the Resilience in Territorial Scenarios

“…Quantitative modelling is represented by stock and flow diagram ( Figure 2). Stock is the first basic building block in SDM and it represents the variable which describes the condition of the system at any particular time [12,50,[57][58][59]. Flow is the second block in SDM and it tells how stocks change over time.…”

“…The main objective of this work is to lay the basis for future compared applications of these two models to the same case study.in order to underline dynamic model (DM) features that are closely related to the decision-making process in territorial and urban planning [11]. DMs are recognized as suitable tools to evaluate policies and actions aimed at increasing urban resilience [12][13][14]. This property is related to the fact that these models are built and grounded on dimensions related to both urban systems and urban resilience.…”

“…This property is related to the fact that these models are built and grounded on dimensions related to both urban systems and urban resilience. In fact, during the construction of the model, it is necessary to select and identify which aspects of a territorial system have to be included with reference to the evaluation goal, from the environmental to the economic dimension [11,12,15]. In this sense, DMs are able to reveal both the dynamic behavior of urban and territorial systems and the impacts of policies on the key variables identified.Specifically, the main aim of this work is to study the principal characteristics of the system dynamics model (SDM) and Lotka-Volterra models (LV) in order to apply both to the same case study.…”

“…The integration of dynamic models with urban simulation methods makes it possible to support data collection and elaboration, problem structuring, and facilitate the involvement of actors and stakeholders [12,88,[101][102][103].…”

Modeling customer satisfaction in microfinance sector: A fuzzy Bayesian networks approach

“…BNs are an artificial intelligence model used to represent and analyze uncertain knowledge from a probabilistic standpoint. 21,[24][25][26][27] This modeling technique has other labels in the literature, such as Bayesian belief networks, causal probabilistic networks, causal networks, and influence diagrams. 7,28 BNs are a directed acyclic graph (DAG) that represents the probabilistic dependencies between a set of variables.…”