Machine learning for ecosystem services

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl

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Dangosydd eitem ddigidol (DOI)

  • Simon Willcock
  • Javier Martinez-Lopez
    Basque Centre of Climate Change
  • Danny Hooftman
    Centre for Ecology and Hydrology, Wallingford, UK
  • Kenneth Bagstad
    US Geological Survey
  • Stefano Balbi
    Basque Centre of Climate Change
  • Alessia Marzo
    University of Cantania
  • Carlo Prato
    University of Cantania
  • Saverio Sciandrello
    University of Cantania
  • Giovanni Signorello
    University of Cantania
  • Brian Voigt
    University of Vermont
  • Ferdinando Villa
    Basque Centre of Climate Change
  • James Bullock
    Centre for Ecology and Hydrology, Wallingford, UK
  • Ioannis Athanasiadis
    Wageningen University
Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64-91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54-77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.

Allweddeiriau

Iaith wreiddiolSaesneg
Tudalennau (o-i)165-174
Nifer y tudalennau19
CyfnodolynEcosystem Services
Cyfrol33 pt B
Dyddiad ar-lein cynnar5 Mai 2018
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - Hyd 2018

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