Comparing Knowledge-Driven and Data-Driven Modeling methods for susceptibility mapping in spatial epidemiology : a case study in Visceral Leishmaniasis
(2014) 17th AGILE International Conference on Geographic Information Science, 2014 p.1-5- 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... (More)
- 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 (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4732672
- author
- Rajabi, Mohammadreza LU ; Mansourian, Ali LU ; Pilesjö, Petter LU ; Hedefalk, Finn LU ; Groth, Roger LU and Bazmani, Ahad
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Visceral Leishmaniasis (VL), spatial epidemiology, prediction, knowledge-driven method, data-driven method., Artificial Intelligence (AI), Geospatial Artificial Intelligence (GeoAI)
- host publication
- Proceedings of the AGILE'2014 International Conference on Geographic Information Science, Castellón, June, 3-6
- pages
- 5 pages
- publisher
- Association of Geographic Information Laboratories for Europe
- conference name
- 17th AGILE International Conference on Geographic Information Science, 2014
- conference location
- Castellon, Spain
- conference dates
- 2014-06-02 - 2014-06-06
- project
- Geospatial modeling and simulation techniques to study prevalence and spread of diseases
- language
- English
- LU publication?
- yes
- id
- 3f2adb14-c323-4061-b1ca-5b3638b711a6 (old id 4732672)
- alternative location
- http://agile-online.org/Conference_Paper/cds/agile_2014/agile2014_126.pdf
- date added to LUP
- 2016-04-04 12:24:18
- date last changed
- 2023-09-05 13:23:08
@inproceedings{3f2adb14-c323-4061-b1ca-5b3638b711a6, 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}}, author = {{Rajabi, Mohammadreza and Mansourian, Ali and Pilesjö, Petter and Hedefalk, Finn and Groth, Roger and Bazmani, Ahad}}, booktitle = {{Proceedings of the AGILE'2014 International Conference on Geographic Information Science, Castellón, June, 3-6}}, keywords = {{Visceral Leishmaniasis (VL); spatial epidemiology; prediction; knowledge-driven method; data-driven method.; Artificial Intelligence (AI); Geospatial Artificial Intelligence (GeoAI)}}, language = {{eng}}, pages = {{1--5}}, publisher = {{Association of Geographic Information Laboratories for Europe}}, title = {{Comparing Knowledge-Driven and Data-Driven Modeling methods for susceptibility mapping in spatial epidemiology : a case study in Visceral Leishmaniasis}}, url = {{http://agile-online.org/Conference_Paper/cds/agile_2014/agile2014_126.pdf}}, year = {{2014}}, }