Bayesian networks as a decision support tool for rural water supply and sanitation sector

Despite the efforts made towards the Millennium Development Goals targets during the last decade, still millions of people across the world lack of improved access to water supply or basic sanitation. The increasing complexity of the context in which these services are delivered is not properly captured by the conventional approaches that pursue to assess water, sanitation and hygiene (WaSH) interventions. Instead, a holistic framework is required to integrate the wide range of aspects which are influencing sustainable and equitable provision of safe water and sanitation, especially to those in vulnerable situations. In this context, the WaSH Poverty Index (WaSH-PI) was adopted, as a multi-dimensional policy tool that tackles the links between access to basic services and the socio-economic drivers of poverty. Nevertheless, this approach does not fully describe the increasing interdependency of the reality. For this reason, appropriate Decision Support Systems (DSS) are required to i) inform about the results achieved in past and current interventions, and to ii) determine expected impacts of future initiatives, particularly taking into account envisaged investments to reach the targets set by the Sustainable Development Goals (SDGs). This would provide decision-makers with adequate information to define strategies and actions that are efficient, effective, and sustainable. This master thesis explores the use of object-oriented Bayesian networks (ooBn) as a powerful instrument to support project planning and monitoring, as well as targeting and prioritization. Based on WaSH-PI theoretical framework, a simple ooBn model has been developed and applied to reflect the main issues that determine access to safe water, sanitation and hygiene. A case study is presented in Kenya, where the Government launched in 2008 a national program aimed to increase the access to improved water, sanitation and hygiene in 22 of the 47 existing districts. Main impacts resulted from this initiative are assessed and compared against the initial situation. This research concludes that the proposed approach is able to accommodate the conditions at different scales, at the same time that reflects the complexities of WaSH-related issues. Additionally, this DSS represents an effective management tool to support decisionmakers to formulate informed choices between alternative actions

Drinking water in Barcelona through the lenses of sustainability

Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.1 - Per a 2030, assegurar l’accés de totes les persones a habitatges i a serveis bàsics adequats, segurs i assequibles, i millorar els barris margina

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

A holistic and participatory information system for rural water and sanitation sector in Latin America and the Caribbean

The provision of water supply, sanitation and hygiene services has emerged as a top priority in the development agenda in Latin American and the Caribbean. In light of the investments envisaged to reach the targets set by the Sustainable Development Goals (SDGs), information systems (IS) will play a key role in improving decision-making. In this context, this article introduces a global IS which is increasingly implemented in a number of countries across Latin America and the Caribbean as a policy instrument to support national and local decision-making: the Rural Water Supply and Sanitation Information System (SIASAR). This information system includes a comprehensive framework for data collection, data analysis and data dissemination that simultaneously fulfils different stakeholders’ needs. This article analyses these three key monitoring issues from the viewpoint of stakeholders’ involvement. Results indicate that SIASAR represents a suitable monitoring framework to analyse sustainable services and the level of service delivered. Additionally, it is highlighted the advantages of adopting a continued participatory approach in system development, namely i) the stimulation of experience exchange and knowledge sharing among recipient counties, ii) the promotion of learnt-by-doing, and iii) an increase of regional understanding, collaboration and comparison.Postprint (published version

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

Bayesian network modelling of hierarchical composite indicators

The water, sanitation and hygiene (WaSH) sector has witnessed the development of multiple tools for multidimensional monitoring. Hierarchical and composite indicators (CI)-based conceptual frameworks provide one illustrative example. However, this approach does not address the existing interrelationship of the indicators they integrate. Bayesian Networks (BNs) are increasingly exploited to assess WaSH issues and to support planning and decision-making processes. This research aims to evaluate the validity, reliability and feasibility of BNs to replicate an existing CI-based conceptual framework. We adopt a data-driven approach and we propose a semi-automatic methodology. One regional monitoring initiative is selected as a pilot study: the Rural Water Supply and Sanitation Information System (SIASAR). Data from two different countries are processed and analysed to calibrate and validate the model and the method. Major findings show i) an improvement of model inference capacity when providing structure to the networks (according to the CI-based framework), ii) a reduction and quantification of the key components that explain a pre-defined objective variable (implying important advantages in data updating), and iii) an identification of interlinkages among these components (which might enhance multi- and trans-disciplinary actions). We conclude that BNs accurately replicates the CI-based conceptual framework. The proposal contributes to its wider application.Peer ReviewedPostprint (author's final draft

Integrating sustainability and social commitment transversal competence across civil engineering curricula through case studies and a common evaluation rubric

The Civil Engineering School of Barcelona has a long tradition applying own experiences from international co-operation for development projects to teaching and learning activities. The continuous work carried out in this line has been supported by three pillars: i) motivated lecturers and professors, ii) institutional framework (including support for teaching innovation and Int. Cooperation for Development), and iii) increased networking with Education for Development (ED) partners. This paper presents a number of teaching materials, i.e. Case Studies, developed within an ED initiative supported by the Municipality of Barcelona during 2016-2017. The approach adopted is aligned with the European Global Dimension in Engineering Education (GDEE) initiative. Specifically, seven case studies are introduced, covering from first year of civil engineering to compulsory/master courses. The case studies include different kind of activities, which can be integrated and evaluated within the course, but also under the framework of a common evaluation rubric divided in three levels (basic, intermediate, final). This rubric makes operative the definition of Sustainability and Social Commitment cross-cutting competence at the Universitat Politècnica de Catalunya, UPC (one of the common competencies to all UPC studies). The teaching materials are the result of collaborative experiences of School’s students, lectures and professors. Some of them have been already implemented successfully in previous years and others are being tested during the year in course. This paper discusses about drivers and barriers for the development, application and consolidation of these materials.Postprint (published version

SIASAR: a country-led indicator framework for monitoring the rural water and sanitation sector in Latin America and the Caribbean

The provision of water supply, sanitation and hygiene services has emerged as a top priority in the development agenda in Latin American and the Caribbean. In light of the investments envisaged to reach the targets set by the Sustainable Development Goals (SDGs), Information Systems (IS) will play a key role in improving decision-making. In this context, this article introduces a country-led and global IS, which is increasingly implemented in a number of countries across Latin America and the Caribbean as a policy instrument to support national and local decision-making: the Rural Water Supply and Sanitation Information System (SIASAR). It includes a comprehensive framework for data collection, data analysis and data dissemination that simultaneously fulfils different stakeholders’ needs. This article analyses these three key monitoring issues from the viewpoint of stakeholders’ involvement. Results indicate that SIASAR represents a suitable monitoring framework to analyse sustainable services and the level of service delivered. Additionally, it is highlighted the advantages of adopting a continued participatory approach in system development, namely i) the stimulation of experience exchange and knowledge sharing among recipient counties, ii) the promotion of learning-by-doing, and iii) an increase of regional understanding, collaboration and comparison.Peer ReviewedPostprint (author's final draft

Data–driven Bayesian networks modelling to support decision–making : application to the context of Sustainable Development Goal 6 on water and sanitation

We live in a complexand interconnected world which permeates ditterent scales. sectors or decision problems. This fact is acknowledged by the United Nations 2030 Agenda for Sustainable Development, which underscores current global challenges, recognizes their interconnectivity and calls for international action. lt is recognized that the connected nature of the issues we currently face have been tackled by "silo" approaches, separating the complexities ofthe real-world into specialized disciplines. fields of research, institutions and ministries, each one focused on a fraction of the overall truth. Similarly, it is widely recognized the need of a major shift in decision-making processes towards more holistic and integrated approaches. Evidence-based decrsion-making involves complexprocesses ofconsidering a wide range of information of different nature. Nowadays, available data can support these processes, but methodologies to effectively integrate these data are lacking. With the aim to contribute in this direction, this thesis focuses on the increasing use of Bayesian Networks (BNs) modelling as an approach to accom m odate com plex problem s and to support decis ion-making. Com mon practica em ploys separately expert knowledge and empirical data to build and apply associated models. Des pite of the demonstrated utility of this practica, in an era where the data are bigger, faster and more detailed than even before, there is still room for further exploration. Thus, this dissertation proposes a data-driven Bayesian Networks approach to combine expert opinion and quantitative data to support informad decision-making. We propose two systematic methods to this end. First. we use our approach to replicate composite indicators (Cl)-based conceptual frameworks, which represent expert knowledge. through the use of structure learning algorithms, which characterizes this data-driven Bayesian Networks approach. Second, we use our approach to identify interlinkages associated with a complex context, coupled with a statistical technique (i.e. bootstrapping) to reduce results uncertainty and with a comprehensive result robustness analysis (i.e. expert knowledge). For testing and validating the proposed approach, this thesis takes the Sustainable Development Goal 6 embedded on the 2030 Agenda as a reference point, with particular attention to the water, sanitation and hygier:ie sector. Our results emphasize the likely utility of the data-driven Bayesian Networks approach adopted. First. it allows the integration of both expert knowledge and data availability when dealing with BNs modelling, and it accurately replicates (Cl)-based conceptual frameworks. As added values, this combination improves model inference capacity, it reduces and quantifies the key variables that explµin a pre-defined objective variable (implying important advantages in data updating), and it identifies the interlinkages among the variables considerad (which might enhance more integrated actions). Second, the approach adopted is useful to accommodate a thorough analysis and interpretation of the complexities and interdependencias of any context at hand. As added values, interlinkages identification is spurred on by the available data and this identification makes the approach more suitable than the use of composite indicators. Third, the systematic nature of the methodological contributions associated with the proposed approach can be adapted to different complexproblems. Thus, it might expand and deepen the knowledge about the validity, reliability and accuracy of using BNs modelling.Vivimos en un mundo complejo e interconectado que impregna diferentes escalas. sectores o problemas de decisi ón. Esta visión es destacada por la Agenda 2030 para el Desarrollo Sostenible de las Naciones Unidas, que además pone de manifiesto los desafíos globales actuales. reconoce su interconexión y hace una llamada a la acción internacional. Por otro lado. es ampliamente reconocido que la naturaleza conectada de los problemas a los que nos enfrentamos actualmente se ha abordado mediante enfoques "estancos". separando las complejidades del mundo real en disciplinas especializadas. campos de investigación, instituciones y ministerios. cada uno centrado en una parte de la verdad. De igual manera. es reconocida la necesidad de un cambio de paradigma en los procesos de toma de decisiones hacia enfoques m ás holísticos e integrados. La toma de decisiones basada en evidencias lleva implícita procesos complejos en los que se integran una amplia gama de información de diferente naturaleza. Hoy en día, los datos cuantitativos disponibles pueden respaldar estos procesos. pero faltan metodologías para integrar estos datos de manera efectiva. Con el objetivo de contribuir en esta dirección, esta tesis se centra en el uso de modelos de Redes Bayesianas (BNs), como un enfoque válido para abordar problemas complejos y, en última instancia, para apoyar la toma de decisiones. En la práctica, se emplea comúnmente por separado el conocimiento de expertos y los datos empíricos para construir y aplicar estos modelos. A pesar de la utilidad de esta práctica, en una era en la que los datos son má¿ numerosos, más rápidos y más detallados que antes, hay espacio para explorar hasta dónde pueden llegar estos datos. En este sentido, esta tesis propone un enfoque de Redes Bayesianas basadas en los datos que permite combinar el conocimiento experto y la información cuantitativa existente para, en última instancia, apoyar la toma de decisiones. Se proponen dos métodos sistemáticos para tal fin. En primer lugar, se emplea dicho enfoque para replicar marcos conceptuales basados en indicadores compuestos (IC), que representan el conocimiento experto, mediante el uso de algoritmos de aprendizaje de estructuras, que caracteriza este enfoque de Redes Bayesianas basado en datos. En segundo lugar, se utiliza el enfoque propuesto para identificar las interrelaciones existentes dentro de un contexto complejo, junto con una técnica estadística (bootstrapping) para reducir la incertidumbre de los resultados y con un análisis bibliográfico exhaustivo (conocimiento experto) para demostrar la robustez de los resultados obtenidos. Para testear y validar el enfoque propuesto, esta tesis toma como punto de referencia el Objetivo de Desarrollo Sostenible 6 que forma parte de la Agenda 2030, con especial atención al sector del agua, saneamiento e higiene. Nuestros resultados ponen de manifiesto la potencial utilidad del enfoque adoptado. Primero, este enfoque permite la integración de conocimiento experto y de información cuantitativa a la hora de construir las RBs, y replica con precisión los marcos conceptuales basados en IC. Como valor añadido, esta combinación mejora la capacidad de inferencia del modelo, y reduce y cuantifica las variables clave que explican una variable objetivo predefinida. En segundo lugar, el enfoque adoptado es útil para dar cabida a un análisis e interpretación exhaustivos de las complejidades e interdependencias de cualquier contexto en cuestión. Como valor añadido, la identificación de las interconexiones se realiza exclusivamente en base a los datos disponibles. Considerar dichas interconexiones hace que este enfoque sea más adecuado que el uso de IC. En tercer lugar. la naturaleza sistemática dé las contribuciones metodológicas asociadas al enfoque propuesto puede adaptarse a diferentes problemas complejos. En este sentido, se considera que se contribuye a ampliar el conocimiento sobre la validez, fiabilidad y precisión del uso de modelos de Redes Bayesianas.Postprint (published version

Bayesian networks as a decision support tool for rural water supply and sanitation sector

Despite the efforts made towards the Millennium Development Goals targets during the last decade, still millions of people across the world lack of improved access to water supply or basic sanitation. The increasing complexity of the context in which these services are delivered is not properly captured by the conventional approaches that pursue to assess water, sanitation and hygiene (WaSH) interventions. Instead, a holistic framework is required to integrate the wide range of aspects which are influencing sustainable and equitable provision of safe water and sanitation, especially to those in vulnerable situations. In this context, the WaSH Poverty Index (WaSH-PI) was adopted, as a multi-dimensional policy tool that tackles the links between access to basic services and the socio-economic drivers of poverty. Nevertheless, this approach does not fully describe the increasing interdependency of the reality. For this reason, appropriate Decision Support Systems (DSS) are required to i) inform about the results achieved in past and current interventions, and to ii) determine expected impacts of future initiatives, particularly taking into account envisaged investments to reach the targets set by the Sustainable Development Goals (SDGs). This would provide decision-makers with adequate information to define strategies and actions that are efficient, effective, and sustainable. This master thesis explores the use of object-oriented Bayesian networks (ooBn) as a powerful instrument to support project planning and monitoring, as well as targeting and prioritization. Based on WaSH-PI theoretical framework, a simple ooBn model has been developed and applied to reflect the main issues that determine access to safe water, sanitation and hygiene. A case study is presented in Kenya, where the Government launched in 2008 a national program aimed to increase the access to improved water, sanitation and hygiene in 22 of the 47 existing districts. Main impacts resulted from this initiative are assessed and compared against the initial situation. This research concludes that the proposed approach is able to accommodate the conditions at different scales, at the same time that reflects the complexities of WaSH-related issues. Additionally, this DSS represents an effective management tool to support decisionmakers to formulate informed choices between alternative actions