Abstract
When dealing with environmental problems, complexity and uncertainty have become barriers to obtaining reliable information to facilitate good decision-making in fire prevention. This paper proposes and tests a workflow to build the structure of a causal model as a decision support system for fire prone peatlands. We extend the generic process of developing a causal model by incorporating knowledge gained from the literatures and from human experts. We outline the process of automatically generating the relevant variables through literature reviews and constructing a causal model using an online survey and discussions with domain experts. We demonstrate that the structure of the causal model developed can provide logical inferences on the spatial and temporal behaviours of these wildfires.
Original language | English |
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Title of host publication | Proceedings of PACIS2018 |
Subtitle of host publication | Pacific Asia Conference on Information Systems (PACIS) |
Editors | Motonari Tanabu, Dai Senoo |
Place of Publication | Atlanta Georgia USA |
Publisher | Association for Information Systems |
Number of pages | 14 |
ISBN (Electronic) | 9784902590838 |
Publication status | Published - 2018 |
Event | Pacific Asia Conference on Information Systems 2018 - Yokohama, Japan Duration: 26 Jun 2018 → 30 Jun 2018 Conference number: 22nd http://pacis2018.org/ (Website) https://aisel.aisnet.org/pacis2018/ (Proceedings) |
Conference
Conference | Pacific Asia Conference on Information Systems 2018 |
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Abbreviated title | PACIS 2018 |
Country/Territory | Japan |
City | Yokohama |
Period | 26/06/18 → 30/06/18 |
Internet address |
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Keywords
- Causal model
- Decision
- Knowledge-based
- Peatland
- Wildfires