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

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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingResearchpeer-review

Abstract

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
Original languageEnglish
Title of host publicationProceedings of the AGILE'2014 International Conference on Geographic Information Science, Castellón, June, 3-6
PublisherAssociation of Geographic Information Laboratories for Europe
Pages1-5
Number of pages5
Publication statusPublished - 2014
Event17th AGILE International Conference on Geographic Information Science, 2014 - Castellon, Spain
Duration: 2014 Jun 22014 Jun 6

Conference

Conference17th AGILE International Conference on Geographic Information Science, 2014
Country/TerritorySpain
CityCastellon
Period2014/06/022014/06/06

Subject classification (UKÄ)

  • Physical Geography

Keywords

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

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