Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

58.848 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament d'Enginyeria Civil i Ambiental
  • Articles de revista
  • View Item
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament d'Enginyeria Civil i Ambiental
  • Articles de revista
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Bayesian network modelling of hierarchical composite indicators

Thumbnail
View/Open
Bayesian network modelling of hierarchical composite indicators_preprint.pdf (4,718Mb)
Share:
 
 
10.1016/j.scitotenv.2019.02.282
 
  View Usage Statistics
Cita com:
hdl:2117/133792

Show full item record
Requejo Castro, DavidMés informació
Giné Garriga, RicardMés informació
Pérez Foguet, AgustíMés informacióMés informacióMés informació
Document typeArticle
Defense date2019-02-19
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
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.
CitationRequejo-Castro, D.; Gine, R.; Pérez-Foguet, A. Bayesian network modelling of hierarchical composite indicators. "Science of the total environment", 19 Febrer 2019, vol. 668, p. 936-946. 
URIhttp://hdl.handle.net/2117/133792
DOI10.1016/j.scitotenv.2019.02.282
ISSN0048-9697
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0048969719307806
Collections
  • Departament d'Enginyeria Civil i Ambiental - Articles de revista [2.622]
  • EScGD - Engineering Sciences and Global Development - Articles de revista [79]
  • Departament d'Enginyeria Química - Articles de revista [1.990]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
Bayesian networ ... te indicators_preprint.pdf4,718MbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina