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You are in Geoinformatics - Creative Commons :: Introduction to Earth System Science Modelling :: Classes 2018

CST-323: Introduction to Earth System Modelling/CAP-465 Modelling and Simulation of Earth Systems (INPE Course 2018)

  • Professors: Mariane Coutinho, Pedro R. Andrade, Gilberto Câmara
  • Lectures: Mondays and Thursdays, 14h00-16h30, Room 12, 1st floor, CCST building

Software

Classes

Title Models Scenarios Concepts Exercises
1 Introduction
2 Modelling Programming Basics
Lua for TerraME Lua scripts nil, number, boolean, string, table, function Lua exercises
3 Systems Dynamics Tub (sysdyn) tub-scenarios (sysdyn) Model, Event, Timer, Chart
4 Feedbacks Coffee, PopulationGrowth (sysdyn) coffee-scenarios, population-scenarios-1, population-scenarios-2 (sysdyn) Environment, instance of Model
5 Epidemics SIR (sysdyn) infection-scenarios-1, infection-scenarios-2, infection-scenarios-3 (sysdyn) Water in the Dam
6 Daisyworld Daisyworld (sysdyn) daisy-calibration (calibration) MultipleRuns (calibration)
7 Chaos ChaoticGrowth (sysdyn), Lorenz (sysdyn)
8 Celular Automata
Cellular Automata Life (ca) Cell, CellularSpace, Neighborhood, Map, Random
Fire in the Forest Fire (ca) Fire in the Forest
Runoff Runoff (gis)
Geospatial Datacabecadeboi.lua (gis) Project, Layer

Papers for Final Projects

The final project consists of an implementation and discussion of one of the models available here or the following papers.

System Dynamics

Scherer A. & McLean A., (2002) Mathematical models of vaccination, British Medical Bulletin 2002;62 187-199.
Energy scenarios for Brazil (in portuguese)

Cellular Automata

S. G. Berjak, J. W. Hearne (2002) An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system. Ecological Modelling 148(2):133–15
C. Beauchemina, J. Samuelb, J. Tuszynskia (2005) A simple cellular automaton model for influenza A viral infections. Journal of Theoretical Biology 232(2) 223–234
S. Hoya White, A. Martín del Rey, G. Rodríguez Sánchez(2007), Modeling epidemics using cellular automata. Applied Mathematics and Computation, 186(1):193-202
Almeida, Rodolfo Maduro, and Elbert EN Macau. "Stochastic cellular automata model for wildland fire spread dynamics." Journal of Physics: Conference Series. Vol. 285. No. 1. IOP Publishing, 2011.
Fisch, Robert, Janko Gravner, and David Griffeath. "Threshold-range scaling of excitable cellular automata." Statistics and Computing 1.1 (1991): 23-39.
Fisch, Robert. "Clustering in the one-dimensional three-color cyclic cellular automaton." The Annals of Probability (1992): 1528-1548.
Li, Wentian. "Complex patterns generated by next nearest neighbors cellular automata." Computers & Graphics 13.4 (1989): 531-537.
Chate, H. & Manneville, P. (1990). Criticality in cellular automata. Physica D (45), 122-135.
Li, W., Packard, N., & Langton, C. (1990). Transition Phenomena in Cellular Automata Rule Space. Physica D (45), 77-94.
Colasanti, R. L., R. Hunt, and L. Watrud. “A simple cellular automaton model for high-level vegetation dynamics.” Ecological Modelling 203.3 (2007): 363-374.
S. Yassemi, S. Dragićevića, M. Schmidt(2008), Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour , Ecological Modelling, 210(1–2), 71–84
Araujo and Celani (20166), Exploring Weaire-Phelan through Cellular Automata: A proposal for a structural variance-producing engine
Rickert, M., Nagel, K., Schreckenberg, M. and Latour, A., 1996. Two lane traffic simulations using cellular automata. Physica A: Statistical Mechanics and its Applications, 231(4), pp.534-550.
White, R. and Engelen, G., 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and planning A, 25(8), pp.1175-1199.
Barredo, J.I., Kasanko, M., McCormick, N. and Lavalle, C., 2003. Modelling dynamic spatial processes: simulation of urban future scenarios through cellular automata. Landscape and urban planning, 64(3), pp.145-160.
Karafyllidis, I. and Thanailakis, A., 1997. A model for predicting forest fire spreading using cellular automata. Ecological Modelling, 99(1), pp.87-97.
Ermentrout, G.B. and Edelstein-Keshet, L., 1993. Cellular automata approaches to biological modeling. Journal of theoretical Biology, 160(1), pp.97-133.
Alarcón, T., Byrne, H.M. and Maini, P.K., 2003. A cellular automaton model for tumour growth in inhomogeneous environment. Journal of theoretical biology, 225(2), pp.257-274.
Yuan, W. and Tan, K.H., 2007. An evacuation model using cellular automata. Physica A: Statistical Mechanics and its Applications, 384(2), pp.549-566.
Dormann, S. and Deutsch, A., 2002. Modeling of self-organized avascular tumor growth with a hybrid cellular automaton. In silico biology, 2(3), pp.393-406.
Bersini, H. and Detours, V., 1994, July. Asynchrony induces stability in cellular automata based models. In Artificial Life IV (pp. 382-387). MIT Press, MA.

Agent-based Modeling

Medeiros, L. C., Castilho, C. A. R., Braga, C., de Souza, W. V., Regis, L., Monteiro, A. M. V. (2011). Modeling the dynamic transmission of dengue fever: investigating disease persistence. PLOS neglected tropical diseases, 5(1), e942.
M Janssen and N.D. Rollins (2012). Evolution of cooperation in asymmetric commons dilemmas. Journal of Economic Behavior and Organization, 81: 220-229. Available in CoMSES Computational Model Library).
S Bandini, F Celada, S Manzoni, G Vizzari (2007). Modelling the immune system: the case of situated cellular agents, Natural Computing, 6(1):19-32.
Pe'er et al. Virtual Corridors for Conservation Management, Conservation Biology (2005): 1997–2003
Garcia et al. Predicting evolution of insect resistance to transgenic crops in within field refuge configurations, based on larval movement. Ecol. Complex. 28, 94–103 (2016).
Malaquias et al. Larval Dispersal of Spodoptera frugiperda Strains on Bt Cotton: A Model for Understanding Resistance Evolution and Consequences for its Management. Scientific reports. 2017 Nov 23;7(1):16109.
Brown, C.; Bakam, I.; Smith. P.; Matthews, R.B., (2016) An agent-based modelling approach to evaluate factors influencing bioenergy crop adoption in north-east Scotland., Global Change Biology Bioenergy, 8, 226-244.
cst-317/classes2018.txt · Last modified: 2018/08/09 08:37 by pedro

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