Visualisation and evaluation of flood uncertainties based on ensemble modelling
2016 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 30, no 2, 240-262 p.Article in journal (Refereed) Published
This study evaluates how users incorporate visualisation of flood uncertainty information in decision-making. An experiment was conducted where participants were given the task to decide building locations, taking into account homeowners’ preferences as well as dilemmas imposed by flood risks at the site. Two general types of visualisations for presenting uncertainties from ensemble modelling were evaluated: (1) uncertainty maps, which used aggregated ensemble results; and (2) performance bars showing all individual simulation outputs from the ensemble. Both were supplemented with either two-dimensional (2D) or three-dimensional (3D) contextual information, to give an overview of the area.The results showed that the type of uncertainty visualisation was highly influential on users’ decisions, whereas the representation of the contextual information (2D or 3D) was not. Visualisation with performance bars was more intuitive and effective for the task performed than the uncertainty map. It clearly affected users’ decisions in avoiding certain-to-be-flooded areas. Patterns to which the distances were decided from the homeowners’ preferred positions and the uncertainties were similar, when the 2D and 3D map models were used side by side with the uncertainty map. On the other hand, contextual information affected the time to solve the task. With the 3D map, it took the participants longer time to decide the locations, compared with the other combinations using the 2D model.Designing the visualisation so as to provide more detailed information made respondents avoid dangerous decisions. This has also led to less variation in their overall responses.
Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 30, no 2, 240-262 p.
Visualisation, uncertainty, flood, ensemble modelling, decision-making
Computer Vision and Robotics (Autonomous Systems) Physical Geography
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-266896DOI: 10.1080/13658816.2015.1085539ISI: 000365550900006OAI: oai:DiVA.org:uu-266896DiVA: diva2:869174
FunderEU, European Research Council, 170430