Spatio-temporal modelling of the spread of common reed on the Finnish coast

Anas Altartouri*, Ari Jolma, Päivi Korpinen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

The common reed (Phragmites Australis) has overtaken the Finnish coast of the Gulf of Finland in many places influencing the coastal ecology and reducing the recreational value of the coast. Our research aims at developing a spatio-temporal simulation model of the spread of reed. In order to account for the dynamic nature of the spread, a cellular automata model is employed. We investigate in depth the explanatory variables and their relevance to the phenomenon. It was found that water depth, proximity to river mouths, and sea openness are significant explanatory variables of the phenomenon. A predictive logistic regression model is suggested to aggregate the explanatory variables and provide transition rules for the cellular automata. The results of the regression model are presented and discussed, raising the necessity of incorporating Bayesian inference in the logistic regression model. Therefore, more insightful likelihood functions for the explanatory variables are provided and discussed.

Original languageEnglish
Title of host publicationModelling for Environment's Sake
Subtitle of host publicationProceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010
Pages213-223
Number of pages11
Volume1
Publication statusPublished - 1 Dec 2010
MoE publication typeA4 Article in a conference publication
EventBiennial Conference of the International Environmental Modelling and Software Society: Modelling for Environment's Sake - Ottawa, Canada
Duration: 5 Jul 20108 Jul 2010
Conference number: 5

Conference

ConferenceBiennial Conference of the International Environmental Modelling and Software Society
Abbreviated titleiEMSs
CountryCanada
CityOttawa
Period05/07/201008/07/2010

Keywords

  • Bayesian inference
  • Cellular automata
  • Gulf of Finland
  • Logistic regression
  • Phragmites australis

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