In this work, several modelling approaches are explored to
represent spatial pattern dynamics of aquatic populations in
aquatic ecosystems by the combination of models, knowledge and data
in different scales.
It is shown that including spatially distributed inputs
retrieved from Remote Sensing images, a conventional
physically-based Harmful Algal Bloom model can be enhanced. Also,
Cellular Automata based models using high resolution photographs
prove to be good in representing aquatic plant growth. Multi-Agent
Systems can capture well the spatial patterns exhibited in GIS
density maps. A synthesis modelling framework was developed to
include biological/ecological growth and diffusive processes, and
local effects in conventional modelling framework. The results of
the complementary modelling paradigms investigated in this research
can be of help in achieving a sustainable environmental management
strategy.
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