Evaluation of Bayesian Networks in Participatory Water Resources Management, Upper Guadiana Basin, Spain

Stakeholder participation is becoming increasingly important in water resources management. In participatory processes, stakeholders contribute by putting forward their own perspective, and they benefit by enhancing their understanding of the factors involved in decision making. A diversity of modeling tools can be used to facilitate participatory processes. Bayesian networks are well suited to this task for a variety of reasons, including their ability to structure discussions and visual appeal. This research focuses on developing and testing a set of evaluation criteria for public participation. The advantages and limitations of these criteria are discussed in the light of a specific participatory modeling initiative. Modeling work was conducted in the Upper Guadiana Basin in central Spain, where uncontrolled groundwater extraction is responsible for wetland degradation and conflicts between farmers, water authorities, and environmentalists. Finding adequate solutions to the problem is urgent because the implementation of the EU Water Framework Directive requires all aquatic ecosystems to be in a “good ecological state” within a relatively short time frame. Stakeholder evaluation highlights the potential of Bayesian networks to support public participation processes

The use of participatory object-oriented Bayesian networks and agro-economic models for groundwater management in Spain

This paper describes the development of a participatory decision support system for water management in the Upper Guadiana basin in central Spain where there has long been competition for groundwater resources between the agricultural sector and the environment. In the last few decades the rapid development of irrigation has led to the over-exploitation of the Mancha Occidental aquifer, the main water source in the area; this in turn has led to the loss of ecologically important wetlands. Against this background the River Basin Authority (RBA) has designed a new water management plan aimed at reducing water consumption. The objective of this paper is to evaluate the impact of these measures on both the environment and the agricultural sector. To this end stakeholders have been invited to actively participate in the development of a decision support system (DSS) based on the combination of an agro-economic model and an object-oriented Bayesian network. This DSS has been used to evaluate the trade-off between agriculture and the environment for different management options at different scales. Results indicate that achieving even a partial recovery of the aquifer water levels will require strict enforcement by the RBA of water restrictions on farmers combined with a high offer price for the purchase of water rights. However, compliance with water restrictions inevitably leads to losses in farm income, especially in small vineyard farms, unless additional measures are taken to compensate for those potential losses. The purchase of water rights alone is insufficient to ensure the recovery of water levels; accompanying measures included in the new regional management plan will also need to be undertaken

A novel planning approach for the water, sanitation and hygiene (WaSH) sector: the use of object-oriented bayesian networks

Conventional approaches to design and plan water, sanitation, and hygiene (WaSH) interventions are not suitable for capturing the increasing complexity of the context in which these services are delivered. Multidimensional tools are needed to unravel the links between access to basic services and the socio-economic drivers of poverty. This paper applies an object-oriented Bayesian network to reflect the main issues that determine access to WaSH services. A national Program in Kenya has been analyzed as initial case study. The main findings suggest that the proposed approach is able to accommodate local conditions and to represent an accurate reflection of the complexities of WaSH issues, incorporating the uncertainty intrinsic to service delivery processes. Results indicate those areas in which policy makers should prioritize efforts and resources. Similarly, the study shows the effects of sector interventions, as well as the foreseen impact of various scenarios related to the national Program.Preprin

Bayesian networks for spatio-temporal integrated catchment assessment

Includes abstract.Includes bibliographical references (leaves 181-203).In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling

Setting the Port Planning Parameters In Container Terminals through Bayesian Networks

The correct prediction in the transport logistics has vital importance in the adequate means and resource planning and in their optimisation. Up to this date, port planning studies were based mainly on empirical, analytical or simulation models. This paper deals with the possible use of Bayesian networks in port planning. The methodology indicates the work scenario and how the network was built. The network was afterwards used in container terminals planning, with the support provided by the tools of the Elvira code. The main variables were defined and virtual scenarios inferences were realised in order to carry out the analysis of the container terminals scenarios through probabilistic graphical models. Having performed the data analysis on the different terminals and on the considered variables (berth, area, TEU, crane number), the results show the possible relationships between them. Finally, the conclusions show the obtained values on each considered scenario

Exploring the interlinkages of water and sanitation across the 2030 Agenda: a Bayesian Network approach

The 2030 Agenda for Sustainable Development recognizes the indivisible and integrated nature of its 17 Sustainable Development Goals (SDGs) and 169 targets, as well as the need to address these interlinkages to fully achieve its aims. In addition, the Agenda stresses the importance of “leaving no one behind”, which can only be achieved by understanding the interlinkages between the Goals and by undertaking actions to bring them together for the benefit of all. Thus, the identification of these linkages will enable countries to implement the SDGs effectively by harnessing synergies between them while managing potential conflicts. Despite their significance in monitoring initiatives, indicators separately are not adequate to provide an insight into the complex cause and effect relations within global development issues. The suitability of Bayesian Networks (BNs) to integrate multiple and simultaneous relationships has been largely exploited in the literature. Taking a dedicated goal on water and sanitation (SDG 6) as starting point, this paper reviews the potential of a BNs approach to analyse the interdependency between the SDGs, the associated targets and the corresponding indicators. Available global data has been exploited to run the BNs model. Achieved results are compared with a recent research developed by UN-Water, where interlinkages between the targets under Goal 6 and other targets across the 2030 Agenda are conceptually described. The paper discusses the extent to which a BNs is a suitable system to identify and assess these linkages, relationships and synergies. The study concludes that a BNs approach is useful to accommodate the complexities and interdependencies of the SDGs targets and indicators.Postprint (published version

Applying Bayesian belief networks (BBNs) with stakeholders to explore and codesign options for water resource interventions

Bayesian Belief networks (BBNs) are a useful tool to account for uncertainty and can be used to incorporate stakeholder understandings of how a system works. In this study, BBNs were applied to elicit and discuss local stakeholders’ concerns in conflicts over water resource planning in two cases in southern Thailand. One concerned the construction of a dam proposed by a top-down project. The other concerned a bottom-up participatory process at the catchment scale to assess the need for water resources interventions and explore perceptions on alternative design options. In the top-down project, the responses of participants during the elaboration of the BBN showed that potentially affected stakeholders were particularly concerned about limited consultation and lack of shared benefits, which led them to oppose the dam project. In the bottom-up project, local stakeholders expected and agreed with the benefits of a dam, proposing to locate the dam upstream of community land. The BBN method did not facilitate dialogue in the top-down dam-building project because no alternative design options could be discussed and potentially affected stakeholders did not want to discuss compensation because of mistrust and differences in valuation of effects. In the bottom-up project, the BBN method did facilitate dialogue on alternative intervention options and their effects. The replicable BBN framework can support policy-makers to better understand water conflict situations in different stages of planning. Its application supports exploring a wider repertoire of options, enlarging the scope for more inclusive and sustainable solutions to water resource conflicts

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

[EN] With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 di erent combinations of a global climate model (GCM)¿regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041¿2070) and long-term (2071¿2100) periods with respect to the baseline (1983¿2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between di erent GCM¿RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.Sperotto, A.; Molina, J.; Torresan, S.; Critto, A.; Pulido-Velazquez, M.; Marcomini, A. (2019). Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks. Sustainability. 11(17):1-34. https://doi.org/10.3390/su11174764S1341117RES/70/1. Transforming our World: The 2030 Agenda for Sustainable Developmenthttps://sustainabledevelopment.un.org/post2015/transformingourworldPasini, S., Torresan, S., Rizzi, J., Zabeo, A., Critto, A., & Marcomini, A. 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Diseño de un modelo de planificación de zonas de actividades logísticas mediante el empleo de redes bayesianas.

La dificultad para el establecimiento de pautas de dimensionamiento de Plataformas Logísticas y, en especial, de las Zonas de Actividades Logísticas (ZAL), recae en la heterogeneidad del desarrollo de este tipo de nodos de transporte a nivel internacional. Bajo la denominación genérica de Plataformas Logísticas han surgido multitud de iniciativas en la escena internacional, que, respondiendo a diferentes motivos de implantación de un nodo de intercambio modal, ha producido la aparición de diferentes tipos de Plataformas con diversos objetivos que implican unidades funcionales específicas, con necesidades de localización, instalación y superficie necesaria deferentes. Este sector logístico tan importante, se encuentra sin metodologías, herramientas o programas que permitan establecer los parámetros de planificación y explotación óptimos para las diferentes zonas de actividades logísticas, si bien se han desarrollado tecnologías de trazabilidad de la carga y elementos basadas en la planificación logística, con el objetivo de determinar los parámetros óptimos de explotación y planificación portuaria, a través de la clasificación de las zonas de actividades logísticas, añadiendo la inferencia de escenarios virtuales. Como resultado principal se destaca que, mediante el empleo de herramientas de inteligencia artificial, modelos gráficos probabilísticos: Redes Bayesianas (BN), se han definido las principales variables de planificación de las zonas de actividades logísticas y se puede planificar con BN una ZAL seleccionando las variables conocidas y obteniendo las variables a predecir