Environmental Data Analysis and Simulation
The goals of Environmental Data Analysis and Simulation are the following:
- Help structure the definition of environmental problems;
- Contribute for its solution conceptualizing simulation, optimization and decision models;
- Implement solutions using computational tools;
- Present solutions using animation and visualization methods.
António da Nóbrega de Sousa da Câmara, Francisco Manuel Freire Cardoso Ferreira
Weekly - 5
Total - 90
- Antonio Camara, Environmental Systems, Oxford University Press, New York, 2002
- Afonso A., Nunes C., 2010. Estatística, Probabilidades: Aplicações e Soluções. Escolar Editora.
- Berthouex, P. M. and L.C. Brown, 1994. Statistics for Environmental Engineers, Lewis Publishers, Boca Raton, 335 pp.
- Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold, New York.
- Montgomery, D.C. and Runger, G. C., 2002. Applied Statistics and Probability for Engineers, 3rd edition, John Wiley & Sons Inc., New York, 720 pp.
- Marôco, J., 2010. Análise Estatística com o PASW (ex-SPSS), Report Number
- Moore, D.S. and G.P. McCabe, 2002. Introduction to the Practice of Statistics, 4th edition, W.H. Freeman and Company, New York, 828 pp.
Teaching is based on an immersion method where homeworks and projects are essential to complement classroom activity
This course does not have any exam and it is based in the effort developed along the semester. There is no obligatory attendance. Final grade = 50% statistics component + 50% simulation/optimization component. Statistics component = individual test (30%) + group works (2) (20%); Simulation component = group works (2) (50%)
There is no minimum grade in any of the evaluation components. All grades acocunted, the grade should be at least 9,5 values.
Group works should have 2 or 3 members.
- Data Collection - Sampling
- Data collection - sampling, spatial and temporal correlation
- Data visualization
- Processing of data in Excel and databases
- Linear Regression
- Analysis of variance
- Principal component analysis and time series
- Diagrams and causal modeling
- Basics of differential equations and application of the Runge-Kutta
- Network management
- Individual interaction and discussions with students
- Integrated approach to environmental problems
Programs where the course is taught: