Methodology to explore emergent behaviours of the interactions between water resources and ecosystem under a pluralistic approach

García-Santos, Glenda; Brito, Mariana Madruga de; Höllermann, Britta; Taft, Linda; Almoradie, Adrian; Evers, Mariele

doi:10.5194/piahs-379-83-2018

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…However, we did not investigate in detail the causality and the most sensitive variables that influence decision making. Using the DPSIR framework (Giupponi, Sgobbi, Mysiak, Camera, & Fassio, 2008) and qualitative system dynamics (García‐Santos et al, 2018; Höllermann & Evers, 2019) would make it possible to analyse this causality in detail, providing a comprehensive understanding of the dynamics of interactions among institutions, communities, and floods. The output‐causal diagram could help in making targeted decisions to improve institutional functions and/or social and technical capacity.…”

Section: Ways Forward and Opportunities For Improvementmentioning

confidence: 99%

Current flood risk management practices in Ghana: Gaps and opportunities for improving resilience

Almoradie

Brito

Evers

et al. 2020

J Flood Risk Management

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This article evaluates the current gaps and describes opportunities for improving flood risk management (FRM) in Ghana, West Africa. A mixed-method participatory approach comprising questionnaires, workshops, interviews with key stakeholders, and a systematic literature review were employed. Existing problems, discourses, FRM practices, and opportunities to enhance flood resilience were identified. They provided the basis for outlining potential research directions into ways of tracking these challenges. The results show how different actors perceive FRM in Ghana. The stakeholders interviewed have different, and even contradictory perceptions of the effectiveness of FRM, which are embedded in their diverse storylines. The findings show that Ghana's FRM is still reactive rather than preventive and that research in the field of quantitative hazard and risk assessment is still rudimentary. FRM policies and flood early warning systems (FEWS) are in place, but efforts should be directed towards their implementation and monitoring, investigation of social and technical capacity aspects, and enhancement of institutions' mandates, and coordination. Moreover, the findings illustrate that FRM is moving toward a more constructive engagement of citizens and stakeholders. However, policies and action plans need to consider more inclusive community participation in planning and management to effectively improve their resilience and develop sustainable solutions.

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Section: Ways Forward and Opportunities For Improvementmentioning

confidence: 99%

Current flood risk management practices in Ghana: Gaps and opportunities for improving resilience

Almoradie

Brito

Evers

et al. 2020

J Flood Risk Management

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“…In addition to this, few studies have applied techniques such as the Analytic Network Process (Saaty, 2001) or PCA and FA to quantify, in terms of weight, indicators' linkages (Hou et al, 2014;Vacik et al, 2007;Wolfslehner and Vacik, 2011). Considering both causal chain (mostly DPSIR framework and its derivatives) and causal networks uses, these approaches have been helpful to conceptual framework development (Chandrakumar and McLaren, 2018;Ortiz-Lozano, 2012;Taft and Evers, 2016;Wolfslehner and Vacik, 2011), to scenario assessment (Chung and Lee, 2009;Ramos-Quintana et al, 2018;Scharin et al, 2016) and to structure modelling exercises (García-Santos et al, 2018;Pirrone et al, 2005). The PSR conceptual framework has been also applied to address the linkages between water scarcity and poverty (Pérez-Foguet and Giné-Garriga, 2011).…”

Section: Introductionmentioning

confidence: 99%

Bayesian network modelling of hierarchical composite indicators

Requejo-Castro

Giné‐Garriga

Foguet

2019

Science of The Total Environment

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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.

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“…A few studies have applied techniques, such as the Analytic Network Process (Saaty, 2001) or PCA and FA, to quantify (in terms of weight) the linkages between indicators (Hou et al, 2014;Vacik et al, 2007;Wolfslehner and Vacik, 2011). Both the causal chain (mostly DPSIR framework and its derivatives) and causal networks approaches have been helpful for: i) conceptual framework development (Chandrakumar and McLaren, 2018;Ortiz-Lozano, 2012;Taft and Evers, 2016;Wolfslehner and Vacik, 2011); ii) scenario assessment (Chung and Lee, 2009;Ramos-Quintana et al, 2018;Scharin et al, 2016); and iii) structuring modelling exercises (García-Santos et al, 2018;Pirrone et al, 2005). The PSR conceptual framework has been also applied to address the linkages between water scarcity and poverty (Pérez-Foguet and Giné-Garriga, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Castro¹

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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.

show abstract

Methodology to explore emergent behaviours of the interactions between water resources and ecosystem under a pluralistic approach

Cited by 7 publications

References 17 publications

Current flood risk management practices in Ghana: Gaps and opportunities for improving resilience

Current flood risk management practices in Ghana: Gaps and opportunities for improving resilience

Bayesian network modelling of hierarchical composite indicators

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

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