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Assessing certainty and uncertainty in riparian habitat suitability models by identifying parameters with extreme outputs

Fu, Baihua; Guillaume, Joseph

Description

The aim of this paper is to introduce a computationally efficient uncertainty assessment approach using an index-based habitat suitability model. The approach focuses on uncertainty in ecological knowledge regarding parameters of index curves and weights. A case study determines which of two 15-year periods has more suitable surface water and groundwater regimes for riparian vegetation. The uncertainty assessment consists of defining constraints on index curves and weights. Linear programming...[Show more]

dc.contributor.authorFu, Baihua
dc.contributor.authorGuillaume, Joseph
dc.date.accessioned2015-12-13T22:27:49Z
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/1885/74113
dc.description.abstractThe aim of this paper is to introduce a computationally efficient uncertainty assessment approach using an index-based habitat suitability model. The approach focuses on uncertainty in ecological knowledge regarding parameters of index curves and weights. A case study determines which of two 15-year periods has more suitable surface water and groundwater regimes for riparian vegetation. The uncertainty assessment consists of defining constraints on index curves and weights. Linear programming is used to identify parameters that yield two extreme outputs: maximising and minimising differences between the two periods. Because they are extremes, if both outputs agree on which period is better (e.g. maximum and minimum differences are both positive), then all other models will also agree. Identifying models with extreme outputs prompts learning about the boundaries of our knowledge and identifies patterns about what is considered certain. It helps build an understanding of what is already known despite the high uncertainty.
dc.publisherPergamon-Elsevier Ltd
dc.sourceEnvironmental Modelling and Software
dc.titleAssessing certainty and uncertainty in riparian habitat suitability models by identifying parameters with extreme outputs
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume60
dc.date.issued2014
local.identifier.absfor050200 - ENVIRONMENTAL SCIENCE AND MANAGEMENT
local.identifier.absfor060200 - ECOLOGY
local.identifier.absfor080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
local.identifier.ariespublicationU3488905xPUB3970
local.type.statusPublished Version
local.contributor.affiliationFu, Baihua, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationGuillaume, Joseph, College of Medicine, Biology and Environment, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage277
local.bibliographicCitation.lastpage289
local.identifier.doi10.1016/j.envsoft.2014.06.015
local.identifier.absseo960506 - Ecosystem Assessment and Management of Fresh, Ground and Surface Water Environments
local.identifier.absseo960900 - LAND AND WATER MANAGEMENT
dc.date.updated2015-12-11T08:34:41Z
local.identifier.scopusID2-s2.0-84904355329
local.identifier.thomsonID000341218800021
CollectionsANU Research Publications

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