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Petter Pilesjö

Petter Pilesjö

Professor

Petter Pilesjö

Comparing Knowledge-Driven and Data-Driven Modeling methods for susceptibility mapping in spatial epidemiology : a case study in Visceral Leishmaniasis

Författare

  • Mohammadreza Rajabi
  • Ali Mansourian
  • Petter Pilesjö
  • Finn Hedefalk
  • Roger Groth
  • Ahad Bazmani

Summary, in English

The aim of this study is to compare knowledge-driven and data-driven methods for susceptibility mapping in spatial epidemiology. Our comparison focuses on one of the arguably most important requisites in such models, namely predictability. We compare one data-driven modelling method called Radial Basis Functional Link Net (RBFLN - a well-established Neural Network method) with two knowledge-driven modelling methods, Fuzzy AHP_OWA and Fuzzy GIS-based group decision making (multi criteria decision making methods). These methods are compared in the context of a concrete case study, namely the environmental modelling of Visceral Leishmaniasis (VL) for predictive mapping of risky areas. Our results show that, at least in this particular application, RBFLN model offers the best predictive accuracy

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • Centrum för Mellanösternstudier
  • Centrum för geografiska informationssystem (GIS-centrum)
  • MECW: The Middle East in the Contemporary World

Publiceringsår

2014

Språk

Engelska

Sidor

1-5

Publikation/Tidskrift/Serie

Proceedings of the AGILE'2014 International Conference on Geographic Information Science, Castellón, June, 3-6

Dokumenttyp

Konferensbidrag

Förlag

Association of Geographic Information Laboratories for Europe

Ämne

  • Physical Geography

Nyckelord

  • Visceral Leishmaniasis (VL)
  • spatial epidemiology
  • prediction
  • knowledge-driven method
  • data-driven method.

Conference name

17th AGILE International Conference on Geographic Information Science, 2014

Conference date

2014-06-02 - 2014-06-06

Conference place

Castellon, Spain

Status

Published

Projekt

  • Geospatial modeling and simulation techniques to study prevalence and spread of diseases