Honors Theses
Date of Award
2015
Document Type
Undergraduate Thesis
Department
Computer and Information Science
First Advisor
Dawn Wilkins
Relational Format
Dissertation/Thesis
Abstract
Cellular automata are discrete models that can be used to simulate many physical systems. Cellular automata have been used to model gas diffusion, different types of chemical reactions, population growth, and land use change over time. Recent research into cellular automata networks has shown that if sparse long range connections are added to a cellular automata, then it will exhibit properties of complex networks. Furthermore, research into modeling climate systems has shown that modeling the global climate as a complex network can be used to predict individual climate variables. In this work we attempt to connect these ideas by simulating global climate variables, from the National Center for Environmental Prediction / National Center for Atmospheric Research Reanalysis 1 Dataset, as a cellular automata model and as a cellular automata network model. In our experiments we use neural networks as the cellular automata transition functions, using both single and multi-variable data. The results of our work suggest that cellular automata networks are better at modeling climate variables than standard cellular automata and that cellular automata based modeling is a viable approach to modeling climate data.
Recommended Citation
Robinson, David, "Modelling Global Climate Variables with Cellular Automata Networks" (2015). Honors Theses. 258.
https://egrove.olemiss.edu/hon_thesis/258
Accessibility Status
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