A spatial fuzzy influence diagram for modelling spatial objects’ dependencies: a case study on tree-related electric outages

ABSTRACT

Spatial objects can be interconnected and mutually dependent in complex ways. In Geographical Information Science, spatial objects’ topological relationships are not discussed together with their attributes’ dependencies, and the vagueness of spatial objects is often ignored during the spatial modelling process. To address this, a spatial fuzzy influence diagram (SFID) is introduced. Compared to the traditional statistical or fuzzy modelling approach, the influence diagram brings advantages in helping decision-makers structure complex interdependency problems. A questionnaire was developed to evaluate the applicability of using an influence diagram in modelling spatial objects’ dependencies. As a case study, an SFID is applied to tree-related electric outages. The result of the case study is represented as a vulnerability map of electrical networks. The map shows areas at risk due to tree-related electric outages. The results were first validated by using a visual comparison of the vulnerability map and electricity fault data. In the second validation step, the percentage of fault data, which has received values in different vulnerability categories, was calculated. The results of the case study can be used to support the decision-making process of electrical network maintenance and planning.

KEYWORDS:

• Influence diagram
• spatial fuzzy influence diagram
• critical infrastructure
• tree-related electric outages
• fuzzy logic

Acknowledgements

We would like to acknowledge the National Emergency Supply Agency of Finland for funding this research as well as Tero Kauppinen and Christian Fjäder for participating in the resulting project. We would also like to express thanks to Jari Ahlstedt from Caruna for providing us with help during the case study. Additionally, we would like to thank our academic colleagues at Aalto University, Hannes Seppänen and Pekka Luokkala, for their support and co-operation throughout the project.

This material is also based in part upon work supported by the U.S. National Science Foundation under grant numbers 1047916 and 1443080. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We would like to acknowledge the CyberInfrastructure and Geospatial Information Laboratory (CIGI) colleagues and visiting professors from Dalian University of Technology (P. R. China), the Chinese Academy of Science, and Xihua University (P.R. China) for participating in the evaluation survey. Finally, we would like to thank Rebecca Vandewalle, who did the language editing for this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

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Additional information

Funding

We would like to acknowledge the National Emergency Supply Agency of Finland for funding this research. This material is also based in part upon work supported by the U.S. National Science Foundation under grant numbers [1047916 and 1443080]. The National Emergency Supply Agency of Finland funded the Alvar project. This article is the research outcome of the Alvar project.